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Added July 26, 2026
What the posting requires: - 3 to 6 years in software or ML engineering, with at least one full cycle on premises or enterprise deployment delivered end to end. - Production grade experience with Python, Docker, Linux systems administration, REST APIs and CI/CD pipelines. - Hands on experience with LLM inference stacks such as vLLM, TGI or Ollama, plus RAG architectures and vector stores. - Experience deploying in constrained environments: air gapped networks, limited connectivity, non standard hardware or complex regulatory requirements. - Full stack debugging instinct, comfortable diagnosing across infrastructure, networking and application layers without a specialist to hand. - Demonstrated ability to ship and maintain a working system end to end where reliability was non negotiable. - Proven ability to navigate ambiguous client requirements and make the call without explicit guidance. Nice to have: - Prior experience with strategic or complex enterprise accounts. - MCP server experience or familiarity with agentic frameworks. - Open source projects, side products or entrepreneurial stints showing technical craft and sustained follow through. What the work actually looks like: - Own end to end deployment of Sarvam's full AI stack in client environments, including on premises, air gapped and classified infrastructure. - Serve as technical single point of contact for assigned accounts, from scoping and proof of concept to steady state operations. - Diagnose and resolve integration failures, model drift, inference issues and infrastructure breakdowns without escalation ladders. - Manage deployment pipelines, model serving and environment configuration in constrained settings. - Drive client side adoption through documentation, training and operational handover. - Own client satisfaction, time to value and uptime for your accounts, and flag risks before they become escalations. - Feed field learnings back into the product and the replicable deployment library. What they say about the person: the posting asks for someone who treats ambiguity as the baseline, owns outcomes rather than tasks, and operates in forward deployed environments without daily oversight. It also says plainly that you are expected to be available and responsive when your clients are, which is worth weighing. Location and working pattern: Delhi, embedded with client teams. No remote or hybrid arrangement is stated. Honest fit guidance: this is the most accessible role on today's board by years, but it is not an easy one. The deciding requirement is the full cycle deployment you have already delivered, not the year count. If your experience is building features inside one company's own environment, the constrained deployment requirement will be the gap. No interview process is published.
What the posting requires: - A PhD in Machine Learning, Computer Science or a closely related field, or in the final stages of completion. - 3+ years of research experience post PhD, or equivalent depth. The posting adds that exceptional early career candidates with a strong research record will be considered, so the years figure is not an absolute gate. - First author publications at top tier ML venues such as NeurIPS, ICML, ICLR, ACL, EMNLP or COLM. - Hands on experience pre training transformer based language models from scratch, ideally at 7B parameters or more. The posting says you should be able to describe a training run you owned end to end, including what went wrong and how you debugged it. - Meaningful contributions to the open source LLM ecosystem: research code, model releases, datasets or substantive contributions to widely used projects. - Fluency in PyTorch and comfort with distributed training. You should be able to read a training loop and immediately see where it might be slow, unstable or wrong. - Strong intuition for experimental design: knowing what to measure, what to ablate, and what scale a result needs to hold at before it can be trusted. Nice to have: - Work on novel architectures such as mixture of experts, state space models or hybrid architectures, or non trivial modifications to standard transformers. - Experience with multilingual or multimodal pre training. - Research contributions in post training: RLHF, RLVR, distillation or reasoning models. - A track record of taking a research idea from prototype to a shipped capability. What the work actually looks like: - Drive open ended research on architecture, optimisation, scaling behaviour, training stability and post training recipes for the next generation of foundational models. - Design and execute ablations at scales that genuinely inform large run decisions, including running pre training experiments end to end yourself. - Translate findings into concrete proposals for the next training run, and own those proposals through to production. - Work directly with the infrastructure and data teams, since many of the important research questions live at that boundary. - Read broadly, write internally, and publish externally when the work merits it. Why the role is framed as rare: the posting says you would be one of a small number of people in the world making architectural and training recipe decisions on a frontier scale model run, with the compute and autonomy to back it up. Location and working pattern: Bengaluru. No remote or hybrid arrangement is stated. Honest fit guidance: the 3 year figure is genuinely low for the seniority of the work because it is counted after a PhD. If you do not have a PhD and a first author publication record, this specific posting is not the one to spend an application on, whatever your industry experience.
What the posting requires as mandatory: - A B.Tech, M.Tech or PhD in Computer Science, Electrical Engineering, Statistics, Mathematics or a related field. - 5+ years of experience in data science, analytics or a closely related domain. - Strong foundation in probability, statistics and estimation theory. - Strong programming in Python, with solid fundamentals in object oriented programming, algorithms and data structures. - Deep familiarity with SQL, complex query writing, indexing and database internals, plus working knowledge of at least one NoSQL store. - Experience with data visualisation and analytical storytelling. - Excellent written and verbal communication. - Familiarity with AI powered tools for analytics and software development, including using them for exploratory data analysis, experiment analysis and documentation, and critically evaluating AI generated output for correctness, statistical validity and reproducibility. Preferred skills: - Exposure to cloud platforms and services such as AWS Kinesis, EKS and autoscaling systems. - Experience building lightweight web or service components with Flask or Django. What the work actually looks like: - Design, implement and maintain offline and online evaluation frameworks for AI and ML features. - Define, validate and evolve the KPIs, success metrics and audit methodologies used across teams. - Perform deep error analysis, bias analysis and segmentation to identify failure modes. - Own golden datasets, validation protocols and benchmarking standards. - Conduct large scale analytical studies on feature performance, data quality and system behaviour. - Design and review experiments including offline evaluations, controlled rollouts and A/B tests, ensuring statistical correctness. - Perform post deployment monitoring and regression detection. - Build tools, dashboards and automation to scale audits and reporting, and enable self serve analytics. - Mentor junior data scientists on statistical rigour, experiment design and analytical storytelling. Location and working pattern: Bangalore. This posting does not state the number of office days, though some other Netradyne listings specify a five day on site model, so ask. A note on the employer: Netradyne postings carry a recruitment fraud warning. Official communication comes only from netradyne.com or us-greenhouse-mail.io addresses, and the company never charges candidates a fee. Honest fit guidance: if you want to build models, this is explicitly not that role. It suits someone who is happier proving whether a system works than building it, and who can tell a product team its metric is wrong.
What the posting requires: - 5+ years on ML deployment, with 2+ years specifically on Intel inference stacks. - Production OpenVINO experience, including model conversion, accuracy validation after quantisation, and driver version pinning. - ONNX Runtime execution provider knowledge: when to use the OpenVINO EP versus native OpenVINO, and when to fall back to the CPU EP. - x86 CPU profiling and optimisation with VTune and perf, including comfort reading hot loops at the assembly level when needed. Noted as a strong plus rather than a requirement: - AVX-512 or AMX intrinsics. Bonus points: - Direct prior interaction with the OpenVINO team or Intel ecosystem partners. - Custom OpenVINO operator authoring. What the work actually looks like: - Land Sarvam's edge models on Intel NPU, discrete GPU and integrated GPU inside defined service level agreements. - Own the OpenVINO build and quantisation recipe for the team's models, including the driver version compatibility matrix. - Drive x86 CPU optimisation for the universal fallback path, covering AVX-512, AMX and threading strategy. - Own the Intel device CI pool and regression detection across OpenVINO upgrades. The hardware surface named in the posting: Intel NPU via OpenVINO on Meteor Lake, Lunar Lake and vPro AI PCs, Intel integrated and discrete GPU via OpenVINO and ONNX Runtime, and x86 and AMD64 CPU for fallback paths. Location and working pattern: Bengaluru. No remote or hybrid arrangement is stated. Honest fit guidance: the 2 years on Intel stacks specifically is the real filter, not the 5 years overall. General ML engineering or even CUDA and Nvidia deployment experience does not substitute, because the entire job is the Intel toolchain and its quirks. If you have that background it is a rare seat, since few teams in India run this surface at production scale.
What the posting requires: - 5 to 8 years of experience in security engineering, information security or a compliance focused role. What the work actually looks like, which is where this posting is unusually detailed: Compliance programme ownership: - Own and manage Hevo's compliance certifications end to end, including SOC 2 Type II, ISO 27001, GDPR and other applicable frameworks, across audit cycles, evidence collection and remediation. - Lead internal readiness assessments and gap analyses, and drive remediation roadmaps with Engineering and Infrastructure. - Act as primary point of contact for external auditors, certification bodies and customer security review teams. - Respond to customer security questionnaires, due diligence requests and vendor assessments. Security engineering and controls: - Design, implement and continuously improve security controls across cloud infrastructure, access management, data handling and the software development lifecycle. - Work with DevOps and Engineering to embed security and compliance requirements into CI/CD pipelines, infrastructure as code and deployment practice. - Conduct security risk assessments, vulnerability reviews and internal audits, prioritising findings and driving resolution to deadlines. - Define and enforce policies on data classification, access control, encryption, logging, monitoring and incident response. Policy and governance: - Develop and operationalise security policies, standards and procedures aligned to industry frameworks. - Build and run a compliance awareness and training programme across the company. - Establish continuous compliance monitoring using GRC tooling and automation. - Report compliance metrics, audit findings and risk posture to leadership on a regular cadence. Cross functional work: - Assess compliance implications of new features and infrastructure changes early with Product and Engineering. - Work with Legal and Finance on contractual obligations, data processing agreements and regional regulatory requirements. - Support Sales and Customer Success on enterprise security reviews for security sensitive deals. Location and working pattern: Bengaluru. Office days are not stated. Honest fit guidance: this sits deliberately at the intersection of security engineering and compliance, and the posting calls it high visibility. If you are a pure penetration tester or a pure GRC analyst, only half the role will fit. It suits someone who can both write the policy and change the pipeline that enforces it.
What the posting requires: - 5 to 8 years of experience in a quantitative analyst role, preferably in B2B SaaS, growth analytics or revenue operations. - Proficiency in SQL and experience working with large datasets. - Experience using Tableau, Looker or similar tools to build dashboards and report insights. - Strong communication skills, with the ability to present data to both technical and non technical audiences. - Ability to manage multiple projects simultaneously and drive deliverables with minimal oversight. - Attention to detail, a diagnostic approach to problems, a continuous learning mindset, and the ability to solve complex, open ended problems. Nice to have, listed as a bonus: - Experience with executive or revenue operations reporting. What the work actually looks like: - Query large datasets using SQL to extract and manipulate data. - Maintain and optimise databases on the data warehouse. - Prepare and present weekly business reviews, forecasts and key metric tracking. - Drive analytics projects on customer funnels and lead acquisition, uncover insights and report findings to leadership. - Collaborate with cross functional teams to execute weekly business reviews and track follow up actions. - Lead and manage end to end analytics projects with minimal oversight, and mentor junior team members. - Continuously challenge and improve metrics by aligning them with industry standards. About the company: Hevo is based in San Francisco and Bangalore, has raised $42 million from Sequoia India, Qualgro and Chiratae Ventures, and powers data analytics for more than 2,000 companies. Its stated mission is building technology from India for the world that is simple enough that anyone can unlock the potential of their data. Location and working pattern: Bengaluru. Office days are not stated. Honest fit guidance: this is analytics for the business, not pipeline engineering. If you want to build ETL, look at the data engineering roles on this board instead. If you are strong in SQL and can hold a room of executives through a weekly business review, the fit is direct. No interview process is published.
What the posting requires: - Ideally 5 to 10 years of related experience in a customer facing role, with a minimum of 2 years in pre-sales with enterprise software. - A minimum of 3 years with modern scripting languages such as Python, Node.js or SQL, or popular programming languages such as C, C++, Java or C#, in a professional capacity. - Experience designing scalable and highly available distributed systems, in the cloud and on premises. - Demonstrated ability to review the complex architecture of existing applications with customers and advise on improvements. - Excellent presentation, communication and interpersonal skills, able to convey complex technical and business concepts clearly to both technology and business leadership. - Ability to strategise with sales teams and recommend how to drive a multi threaded account strategy. - Ability to travel up to 25 percent. - A Bachelor's degree or equivalent work experience. Nice to have: - Experience selling databases or deploying applications with a major cloud provider. - Experience with database programming and data models. - Experience in data engineering or AI and ML projects. - Experience transforming legacy systems into modern, scalable stacks. - Understanding of sales methodologies such as MEDDPICC or Command of the Message. - A MongoDB certification or a cloud provider certification. What the work actually looks like: - Design scalable and performant applications, systems and infrastructure for large software projects using MongoDB. - Guide customers on architecture patterns and optimisation strategies, and articulate business value to the relevant stakeholders. - Collaborate with the sales team on account planning, opportunity qualification and pipeline strategy, owning the technical aspects including discovery, demos, proof of value, presentations, sizing and documentation. - Generate demand within the territory through technical initiatives, trade shows and account based marketing events. - Build customer relationships and champions, and relay field feedback to the product team. - Mentor other Solutions Consulting team members. Location and working pattern: the posting states MongoDB is looking for candidates based in Mumbai for its hybrid working model. Honest fit guidance: this is a pre-sales seat, so revenue is part of how the role is judged. But unlike many roles with this title, it asks for real coding and distributed systems design experience, which is why it is on this list. If you want to write production code full time, it is not the right move.
What the posting requires: - 5+ years of pre-sales experience with enterprise software, and technical aptitude. - 3+ years of software development experience. - Working knowledge and ability to code with one or more modern scripting languages such as Python, Node.js or SQL, or popular programming languages such as C, C++, Java or C#. - Working knowledge of UI development with demonstrable skills in semantic HTML, modern CSS and TypeScript, integrated with a framework such as Vue.js, React.js or Angular.js. - Demonstrated working knowledge of using AI agents such as Cursor, v0, Lovable or Windsurf to orchestrate UI designs and interfaces. - Proficiency deploying applications to the cloud with DevOps best practice, ideally AWS. - Experience with benchmarking, profiling and performance tuning of database systems. - Excellent presentation and communication skills. - A Bachelor's degree or equivalent work experience. Nice to have: - Experience selling databases or deploying applications with a major cloud provider. - Experience with database programming and data modelling. - Knowledge of modern technologies and their integration requirements, such as React.js, cloud storage, API management, Kafka and serverless. - A cloud provider certification or a MongoDB certification. What the work actually looks like: - Work with advisory consulting partners on reference architectures, patterns and technical blueprints for making the best use of MongoDB. - Articulate the business value of MongoDB solutions and bridge that to technical value. - Partner with account teams to support accounts from small startups to large enterprises. - Support sales through technical discovery, demos, proof of value, presentations, sizing and documentation of technical decision criteria, across several opportunities in parallel. - Act as a liaison, gathering field feedback and relaying it to Product Management. - Build and maintain relationships with technical partners, influencing their decisions and reinforcing how MongoDB adds value to their clients. - Drive partner Center of Excellence strategies and create demand for MongoDB solutions. - Mentor other pre-sales team members and share best practice. Location and working pattern: listed as India, without a specific city. Confirm the base location and any hybrid expectation with the recruiter. Honest fit guidance: the partner lens is the differentiator here, so experience working with or inside system integrators and consulting partners matters as much as the technical list. The coding and UI requirements are genuinely enforced by the posting, so this is not a slide deck role.
What the posting requires as mandatory: - A B.Tech, M.Tech or PhD in computer science, electrical engineering or a related area. - 6.5+ years of research or industry experience within computer vision and machine learning models. Note this is higher than the Senior in the title implies, and the body of the posting refers to the role as Staff Research Engineer. - In depth understanding of machine learning, both deep learning and classical ML, and of computer vision concepts. - Excellent programming skills: Python is required, C++ is desired. - Ability to take abstract product concepts and turn them into reality. - Ability to innovate on cutting edge problems without documented solutions. - Hands on experience handling edge deployments targeting various hardware platforms. Preferred skills: - Experience with time series data. - Experience with camera calibration, multi view geometry, 3D reconstruction and SLAM. - Experience with road scene understanding: objects, lanes, intersections, signs. - Experience successfully applying machine learning to a real world problem. - Experience conducting successful statistical experiments. What the work actually looks like: - Help design, implement and commercialise driver monitoring and driver assistance algorithms. - Work with very large datasets and deploy new algorithms and models into thousands of vehicles. - Design and commercialise algorithms characterising driving behaviour. - Design, implement and track key metrics, and architect data driven solutions. - Improve the machine learning infrastructure for scalable training and inference. - Innovate and develop proof of concept solutions showcasing novel capabilities. About the team: Netradyne describes its Analytics team as comprising graduates from IITs, IISc, Stanford, UIUC and UCSD. Location and working pattern: Bangalore. This posting does not state the number of office days, though some other Netradyne listings specify a five day on site model. A note on the employer: Netradyne postings carry a recruitment fraud warning. Official communication comes only from netradyne.com or us-greenhouse-mail.io addresses, and the company never charges candidates a fee. Honest fit guidance: treat this as a staff level computer vision role, because the posting body does. The edge deployment requirement is the part people underestimate: this is vision that has to run on a device in a truck, not in a notebook.
What the posting requires: - A B.E, B.Tech, M.E or M.Tech degree with a minimum of 6+ years of experience in software system optimisation. - Proficiency in C and C++, OpenGL, CUDA and Python. - A solid grasp of basic statistics, probability, and machine learning and computer vision concepts. - Experience with ML frameworks including Caffe, TensorRT, OpenCL, SNPE, OpenVINO and ONNX. - Exceptional attention to detail, strong analytical skills and a creative mindset directed at system performance. Described as a valuable asset rather than required: - Experience with embedded platforms, make files and build systems, and familiarity with Jenkins. What the work actually looks like: - Craft and develop tools, frameworks and reporting mechanisms for system optimisation. - Streamline software for deployment across IoT devices, implementing on GPU, CPU and DSP. - Enhance data platforms and optimise the machine learning models deployed on the platform. - Improve driver monitoring and driver assistance algorithms to boost system efficiency, and report the key performance indicators. - Manage production inquiries and ensure overall application stability in production environments. - Convey highly technical results to diverse audiences. Location and working pattern: Bangalore. Office days are not stated in this posting. A note on the employer: as with Netradyne's other listings, the posting carries a recruitment fraud warning. Official mail comes only from netradyne.com or us-greenhouse-mail.io addresses, and the company never asks candidates for fees or for sensitive data over unsecured channels. Honest fit guidance: this is the inference optimisation and embedded deployment end of ML, not model research. If your background is training models in Python and you have never profiled something on a DSP or converted a model through SNPE or TensorRT, the framework list is the honest gap. If you have done that work, it is one of the clearer matches on today's board.
What the posting requires: - 6+ years of experience, including producing high quality production code and detailed technical design documents for distributed systems. - Proven ability to break down complex, multi month projects into actionable milestones and tasks. - Solid grasp of industry best practice for distributed systems: monitoring, documentation, testing and finish quality. - Experience optimising cloud resource utilisation or developing large scale distributed tools. - Ability to make well reasoned trade offs between system performance, development velocity and technical debt. What the work actually looks like: - Lead projects from concept to deployment, designing and building scalable systems that handle high volume data streams for cost attribution and resource management. - Identify and eliminate inefficiencies in the cloud architecture, engineering solutions that mitigate resource waste and improve attribution accuracy. - Act as technical expert and owner for your service area across its full lifecycle, including code quality, monitoring and operational overhead. - Work with product and infrastructure teams to turn business needs into technical specifications, and build tooling that simplifies cloud economics for other engineers. - Contribute to code reviews, share engineering practice and mentor junior engineers. - Drive a shift left culture by integrating efficiency checks directly into the developer workflow. - Build automation and AI or agentic tooling that detects waste, attributes cost and remediates issues with minimal human intervention. The scale described: the team manages infrastructure efficiency across AWS, Azure and GCP for one of the world's largest data and AI platforms, with cloud spend in the hundreds of millions of dollars. Location and working pattern: Bengaluru. Databricks says it is still building out this site, so expect an in office or hybrid arrangement. The number of days is not stated. Compliance note in the posting: where a role requires access to export controlled technology or source code, Databricks says applying for a US government licence is at its discretion and it may decline to proceed on that basis. Honest fit guidance: cost and efficiency engineering is often dismissed as unglamorous, but at this scale it is a genuine distributed systems problem with a measurable outcome. If you have built large scale tooling and enjoy owning a service end to end, six years is a fair bar.
What the posting requires: - 6+ years of experience in data engineering, data platforms and analytics, or software engineering. - Comfort writing code in Python, Scala or JavaScript and TypeScript, and modern frameworks. - Working knowledge of two or more common cloud ecosystems (AWS, Azure, GCP), with expertise in at least one. - Deep experience with distributed computing using Apache Spark, including knowledge of Spark runtime internals. - Familiarity with CI/CD for production deployments. - Working knowledge of MLOps, ML and AI models, and AI APIs. - Experience designing and deploying performant production data architectures and applications combining data pipelines, ML and AI models, and user facing interfaces. - Experience with technical project delivery: managing scope, timelines and measurable outcomes. - Documentation and whiteboarding skills. - Experience working with enterprise clients and managing conflict across a broad stakeholder range. - Willingness to travel to customers about 20 percent of the time. - A Databricks certification. What the work actually looks like: - Lead customer technical projects, delivering production grade systems, reference architectures, custom applications, and data ingestion with ML and AI model integration. - Guide strategic customers through transformational big data projects, from end to end design through build and deployment. - Work with engagement managers to scope technical delivery with customer input. - Own architecture and design decisions, keeping solutions secure, scalable and aligned to both customer needs and Databricks best practice. - Embed with customer teams, engaging with stakeholders from individual contributors to executives. - Work with Databricks engineering and customer support to feed back product and implementation issues and drive rapid resolution. - Contribute accelerators, frameworks and best practices that scale across accounts and influence the product roadmap. Location and working pattern: Bengaluru, with roughly 20 percent travel to customers. Compliance note in the posting: the standard Databricks export controlled technology clause applies. Honest fit guidance: this is a hands on customer facing engineering role, not a solutions architect who only advises. The posting says it is for builders who thrive at the intersection of technology and business impact, and the billable framing is worth taking seriously: delivery to specification and to date is how the work is judged.
What the posting requires as minimum: - A Bachelor's or Master's degree in Computer Science, Information Systems, Engineering, or equivalent practical experience. - 6+ years of Salesforce development experience, including strong hands on expertise in Salesforce CPQ. - Strong understanding of Quote to Cash processes, sales workflows and Salesforce CPQ best practice. - Hands on experience with Apex, Lightning Web Components, Flows, SOQL, and REST and SOAP APIs. - Experience with product modelling, pricing rules, quoting workflows, approvals, amendments and renewals. - Experience integrating Salesforce with external systems such as ERP, billing, payment or other enterprise platforms. What the work actually looks like: - Design, build and support Salesforce CPQ solutions across product modelling, pricing, quoting, approvals, amendments and renewals. - Configure and enhance CPQ capabilities including product rules, price rules, bundles, product options, quote templates, discounting and approval workflows. - Partner with Sales Operations, Revenue Operations, Finance and Deal Desk to understand requirements and close process gaps. - Develop and maintain Salesforce solutions using Apex, Lightning Web Components, Flows, SOQL and platform APIs. - Build and support integrations between Salesforce CPQ and external enterprise systems. - Participate in technical design, story grooming, sprint planning, testing, code reviews, release management and production deployments. - Troubleshoot issues across CPQ configuration, pricing logic, automation, integrations and system performance. - Support documentation, user acceptance testing, regression testing and user enablement. - Ensure solutions align with security, data governance and Salesforce platform best practice. - Use AI powered engineering tools such as Cursor, Claude Code, Agentforce and GitHub Copilot to improve productivity, while applying sound judgement on security, accuracy and responsible use. Location and working pattern: the posting states MongoDB is looking for candidates based in Gurugram for its hybrid working model. Honest fit guidance: this is a specialist Salesforce CPQ role and the 6 years is specifically Salesforce development, not general software engineering. A backend engineer without CPQ experience would be applying against the core requirement. For an experienced Salesforce developer, working on the systems of a company like MongoDB rather than an agency is the draw.
What the posting requires: - 6+ years of software engineering experience, described as being a master of fundamentals. - Full stack versatility: able to move between frontend, backend and infrastructure and own features end to end. - Comfort designing user experience in React and diving into AWS to tune heavy video transcoding pipelines. - Ability to architect and scale cloud environments, including complex AWS infrastructure and ECS services. - Deep observability practice, with precise telemetry across CPU, memory, network and disk IOPS. - Ability to design and optimise data pipelines at scale. - Strong engineering intuition and architectural expertise, since the team leverages agentic coding models heavily and relies on human judgement for whether systems are built the right way. What the work actually looks like: - Own the technical execution for massive scale physical AI training data. - Lead technical development of Instawork Lens, the global data collection platform, and the dedicated QA platform, ensuring high throughput data enrichment, labelling and continuous quality assurance loops. - Architect and scale cloud environments, managing AWS infrastructure and ECS services for video transcoding. - Design and optimise data pipelines capable of operating at scale. - Act as a key engineering leader in the Bengaluru hub. What the business actually does, since it is unusual: Instawork Robotics Labs deploys skilled hourly workers into real commercial and residential environments to capture task data, which leading robotics labs then use to train their foundation models. The posting frames closing this data gap as the defining infrastructure challenge of the physical AI era. Location and working pattern: Bengaluru. Office days are not stated. Honest fit guidance: the posting asks for a generalist and means it, so specialists who have only worked in one layer will struggle against the breadth requirement. Video pipeline and transcoding experience is the part most candidates will lack. If you like owning infrastructure and product surface at once, and the physical AI data problem interests you, six years is a fair bar for the scope described.
What the posting requires: - 7 to 9 years of hands on experience in a DevOps, Site Reliability Engineering or Platform Engineering role. What the work actually looks like, grouped as the posting groups it: CI/CD and automation: - Design, build and maintain CI/CD pipelines supporting rapid, high quality delivery across multiple teams and environments. - Automate build, test, release and deployment workflows. - Own release management including versioning, tagging, rollback strategies and progressive delivery through canary and blue green deployments. - Integrate automated testing, code quality gates and security scanning into delivery pipelines. Cloud infrastructure and architecture: - Architect, provision and manage scalable cloud infrastructure across AWS, GCP or Azure using infrastructure as code tools such as Terraform, Pulumi or CloudFormation. - Design for high availability, fault tolerance and cost efficiency, and review and optimise cloud spend. - Manage multi environment setups across development, staging and production with environment parity. Containers and microservices: - Build and manage containerised workloads using Docker and Kubernetes on EKS, GKE, AKS or self managed clusters. - Define standards for container image builds, registry management, security scanning and image lifecycle. - Implement service mesh, ingress and networking within Kubernetes. - Drive adoption of Helm or Kustomize for repeatable deployments. Observability and reliability: - Build and maintain an observability stack covering metrics, logs and distributed tracing, using tools such as Prometheus, Grafana, Datadog, ELK or EFK, and Jaeger. - Define SLIs, SLOs and error budgets with engineering teams and lead SRE practice across the organisation. - Lead incident response including on call rotation and blameless post mortems. Security and compliance: - Embed security into infrastructure and pipelines: secrets management with Vault or AWS Secrets Manager, least privilege IAM, network segmentation and vulnerability scanning. Who you report to: the Director of IT and Information Security. Location and working pattern: Gurugram. Office days are not stated. Honest fit guidance: the breadth here is wide and genuinely staff level. An on call rotation is stated. ChargePoint notes it provides reasonable accommodations throughout the hiring process and gives an email address for such requests.
What the posting requires: - 7+ years of engineering experience, extensively with Django or Flask, React, and relational databases in a microservice based architecture. - A Bachelor's degree in Computer Science or Computer Engineering, stated as preferred. - Strong experience with AWS including Route53, CloudFront, API Gateway, Aurora RDS, DynamoDB, ElastiCache, EKS, ECS, S3, the AWS SDK, Lambda and SQS. - Proficiency writing unit and integration tests, with hands on experience across complete CI/CD tooling including Git, CI/CD tools and security scans. - Knowledge of Linux administration, comfortable with console commands, scripting and utilities such as Python and Bash. - Demonstrated experience building REST APIs and using OpenAPI or Swagger specifications. - Knowledge of Kafka or Flink. - Proven experience with agile development, CI/CD tools and project tracking tools, plus familiarity with gRPC, GraphQL and automation tools such as Ansible, Chef or Puppet. - Experience with third party integrations, and custom application development and deployment. - Proficiency across SQL and NoSQL stores including MySQL, Postgres, Redis, DynamoDB, ElasticSearch and MongoDB. - Experience with BI tools such as Tableau or Power BI. Desired rather than required: - Experience with large scale, technically complex accounts that constantly challenge product capabilities. - Demonstrated ability to communicate, present and influence at all levels of an organisation. What the work actually looks like: - Design, develop and maintain cloud native solutions and analytics dashboards for proactive monitoring and network hygiene. - Ensure appropriate documentation for all development and modification. - Ensure the integrity and security of institutional data. - Design data flow diagrams to define workflow, and data structure diagrams to refine database needs. - Translate software requirements into low level design, and evangelise clean architecture and clean code practice. - Perform code reviews, and help train, guide and lead others in building projects start to finish. Who you report to: the Director, NOC Delivery and Automation. Location and working pattern: Bengaluru, India. Office days are not stated. Honest fit guidance: the requirement list is long but the core is Python web services on AWS at senior level. Do not be put off by the breadth of named technologies, which reads more like a wish list than a checklist. The NOC context means the software has real operational consequences when a charger is down.
What the posting requires: - 8 to 12 years designing, building and troubleshooting distributed computing applications, with 4+ years delivering production scale Spark, ML or AI solutions in Python, Java or Scala. - 3 to 5 years in customer facing roles such as Technical Account Manager or Solutions Architect, with strong communication and relationship building. - Hands on expertise with data lakes, SQL based databases, and cloud data warehousing or ETL tools such as Snowflake, Redshift or BigQuery. - Deep knowledge of Spark core internals, Delta or Iceberg, JVM optimisation and memory management. - Proficiency in AI ecosystems including machine learning, deep learning and generative AI. - Practical experience with AWS, Azure or GCP, plus building and managing CI/CD pipelines, monitoring and alerting. - Proven ability to anticipate, identify and mitigate risk while planning solutions for production problems. - Proven ability to work with cross functional teams and senior leadership. What the work actually looks like: - Advanced troubleshooting and root cause analysis on performance and reliability issues across Spark, SQL, Delta, Streaming and Databricks runtime features, using Spark UI metrics, Mosaic AI Model Service, DAGs and event logs. - Work with R&D and network operations teams to define continuous monitoring that catches performance problems early in customer environments. - Build rapid proofs of concept, then test, deploy and monitor solutions. - Develop playbooks and maintain a knowledge base of common issues and solutions for Spark, ML and AI workflows. - Train customer engineering and business teams on performance tuning, debugging and platform features. - Advocate for customers in business review meetings as their trusted advisor and primary technical contact. - Work onsite with field engineering, sales and product teams during customer engagements and technical presentations. How this differs from the senior version of the same role, which is also on this board from an earlier edition: the staff posting raises the distributed computing requirement from 5 to 8 years up to 8 to 12, while keeping the same 3 to 5 years of customer facing experience. If you are between those bands, apply to the senior one. Location and working pattern: Bengaluru, with onsite collaboration during customer engagements. Compliance note in the posting: the standard Databricks export controlled technology clause applies. Honest fit guidance: both halves are required. Deep Spark internals without customer facing experience, or account skill without the ability to read an event log, will each fall short.
What the posting requires: - 8+ years of software engineering experience, with 4+ years focused on AI and ML systems in production. - A B.Tech, B.E. or M.S. in Computer Science, Machine Learning or a related field from what the posting describes as a top tier institution. - Deep expertise in Python, FastAPI, Django and modern backend frameworks for AI service development. - Hands on LLM engineering: LangChain, LangGraph, Amazon Bedrock or OpenAI APIs, prompt engineering and RAG architectures. - Strong Elasticsearch experience including vector search, hybrid search combining BM25 with dense embeddings, and semantic retrieval. - Proficiency with vector databases such as Qdrant, ChromaDB or Pinecone, and embedding based retrieval. - Experience building production LLM systems with a focus on low latency inference, caching strategies and observability. - Strong foundation in distributed systems design, microservices and event driven patterns with Kafka or RabbitMQ. - Experience with AWS or GCP, containerisation with Docker and Kubernetes, and CI/CD pipelines. - Strong knowledge of PostgreSQL including query optimisation and schema design, plus Redis, MongoDB and message queues. - A track record of optimising system performance with measurable improvements in latency or cost. - Experience with voice AI systems, telephony integrations such as Genesys or SIP, and speech processing pipelines. - Background in computer vision, image processing or visual transformer architectures. - Data engineering experience with Airflow, DBT and analytics platforms such as ClickHouse, Trino or Iceberg. - Experience with massive datasets, described as 50TB and above, and building scalable data pipelines. - Hands on experience with model fine tuning techniques such as LoRA or QLoRA. - Familiarity with RLHF style preference tuning and model alignment. - Experience with observability and monitoring stacks covering metrics, logging and tracing. - Certifications in generative AI, agentic AI or a related specialisation. What the work actually looks like: - Lead development of AI solutions across voice AI, computer vision and conversational AI. - Architect and build production grade AI systems that enhance the monitoring and analytics platform, improve customer support, and enable intelligent automation across the charging infrastructure. - Work with cross functional teams to design, build and deploy AI solutions affecting EV drivers and operators worldwide. Who you report to: the Director, NOC Delivery and Automation. Location and working pattern: Bengaluru, India. Honest fit guidance: this is a long requirement list even by staff standards, and the voice AI plus computer vision plus LLM combination is unusually broad. Treat the first block as the real bar and the rest as a preference list. If you have shipped production LLM retrieval systems and can talk about latency and cost numbers, apply.
What the posting requires as minimum qualifications: - A minimum of 8+ years of experience in technical support, with at least 5 years focused on DLP and CASB solutions and escalations. - A foundational understanding of AI and ML technologies, and experience leveraging, securing or positioning AI driven solutions within your domain. Zscaler describes itself as an AI forward enterprise and puts this first in its list. - Experience with data loss prevention, securing SaaS, and Cloud Access Security Brokers. - Practical working knowledge of AWS, Azure or GCP, paired with a strong understanding of Unix, Linux and Windows operating systems. - Deep understanding of data protection principles and practices. Preferred qualifications: - Experience deploying or supporting AI powered sensitive data discovery tools or automated data classification models to enhance DLP capabilities. - Experience with scripting and automation using Python or PowerShell. What the work actually looks like: - Serve as the primary point of contact for escalated DLP issues, providing advanced troubleshooting and resolution. - Take ownership of complex and critical cases through to effective resolution. - Perform detailed root cause analysis to identify underlying issues and implement long term solutions. - Work closely with product development, engineering and other cross functional teams to resolve issues and improve Zscaler DLP products and services. - Maintain clear and proactive communication with customers throughout the escalation, providing regular updates and managing expectations. Who you report to: the Senior Manager, Product Support, in the Support department. Location and working pattern: the posting states this is a hybrid role based in Bangalore. What they say about the person: the posting looks for someone comfortable in ambiguity, with an ownership mindset, a genuine growth mindset and an appetite for feedback. Zscaler frames its culture around impact over activity and constructive, honest debate. Honest fit guidance: the 5 years specifically on DLP and CASB is the real filter, not the 8 years overall. General network security support experience without DLP depth will not clear it. Unlike Zscaler's other escalation roles this one does not state a 24x7 on call rotation in the same terms, but escalation work in a TAC generally carries out of hours obligations, so confirm that before accepting.
What the posting requires: - 9+ years of experience across software engineering, network security and AWS security. Note that it is the combination that is being asked for, not nine years in any one of them. - A proven track record as an expert in the infrastructure security domain. What the work actually looks like: Leadership and strategy: - Develop and document comprehensive security policies, reference architectures, design patterns and the strategic roadmap for infrastructure security. - Keep abreast of current network and infrastructure security trends and best practice. - Collaborate with engineering, AWS and architecture teams so proposed designs meet both business objectives and stringent security requirements. - Partner with stakeholders across the organisation to stay current on infrastructure designs. Technical design and architecture: - Provide technical leadership to build security into all infrastructure and network designs across AWS cloud, SaaS applications and IT environments. - Design and re architect the AWS infrastructure to implement robust security measures, including network segmentation and secure access. - Build defence in depth, secure access and IAM from the ground up. - Address vulnerabilities and misconfigurations across operating systems, infrastructure and cloud. Operations and mentoring: - Work closely with security operations and incident response teams, providing technical leadership. - Mentor other junior infrastructure security professionals. Who you report to: the VP of Information Security. What the company does, for context: Observe.AI's platform lets businesses deploy specialised AI agents that autonomously handle customer conversations, support frontline teams and optimise operations, with each agent built for a specific role. Location and working pattern: Bengaluru. Office days are not stated. Honest fit guidance: the phrase to weigh is from scratch. This is a build the function role rather than a maintain the function role, which suits someone who wants architectural ownership and is comfortable without an existing playbook. If you want mature tooling and established process, this is the opposite of that. The AWS depth is not optional, since the infrastructure being secured is AWS.
What the posting requires: - 10+ years of experience working on enterprise data lakes and warehouses. - 5+ years of Spark and Python experience. - 5+ years of direct hands on experience with AWS or GCP. - Thorough AI knowledge, particularly with codegen tools and agentic frameworks. - Hive, Iceberg, Glue or other technologies that expose big data as tables. - Familiarity with big data file types such as Parquet, Avro and JSON. Nice to have: - Exposure to real time or streaming data technologies. What the work actually looks like: - Guide the data engineering team on building high performance ETL pipelines using Spark and other big data technologies. - Help design the architecture of the internal data platform to support a robust medallion architecture. - Provide thought leadership on achieving infrastructure cost savings on cloud hyperscalers. - Design and build AI agents that automate common development and support tasks the team performs. - Work with Security and Compliance teams so datasets carry appropriate permissions and regulatory controls. - Work with the Data Platform and Governance sibling teams to keep data scalable, consumable and discoverable. The ramp the employer publishes: - In 3 months: a thorough understanding of the architecture of MongoDB's internal data and AI ecosystem. - In 6 months: ownership of the delivery of a large project from scoping and design through to delivery. - In 12 months: having designed new features, led development work, and become a go to expert on parts of the system. Location and working pattern: the posting states MongoDB is looking for candidates based in Gurgaon for its hybrid working model. Honest fit guidance: at 10 years plus this is a technical leadership seat without being a management one, and the AI agent building brief is a genuine part of the job rather than a bolt on. If your data engineering experience is deep but you have not touched agentic frameworks or codegen tooling, that gap is stated explicitly in the requirements and is worth closing before applying.
What the posting requires: - 10+ years of overall industry experience, with 6+ years in backend, services and distributed systems in Go and/or Python, covering services, APIs and data pipelines you have built and operated in production. - Experience with telemetry, event or data pipelines and the storage and query systems behind them. - Expertise with Kubernetes and AWS for deploying, scaling and operating production services. - Understanding of REST, HTTPS and TLS, and designing and shipping secure APIs. - Fluency with agentic coding systems and genuine curiosity about agent harnesses such as Claude Code, Pi and Codex. The posting notes it is a bonus if you have instrumented one. - Expertise with data structures and algorithms, and strong communication skills. - A BS in Computer Science or equivalent is required, and an MS or equivalent is preferred. What the work actually looks like: - Build the configuration backend service that manages and delivers configuration to clients. - Build the telemetry pipeline: ingest usage events, run a multi category classifier, aggregate performance, security and quality patterns, and feed per user and executive dashboards. - Design and ship reliable, observable, secure services and APIs in Go and Python. - Partner with the ML team that consumes the data, the endpoint team that emits it, and partner organisations on the dashboard front end. What the posting says is in it for you, in its own framing: - You build the measurement engine, and the team's whole strategy of optimising where the data points runs on the pipeline you own. - The backend function is net new, so you set the patterns rather than inherit them. - Netskope's customer footprint gives production signal at a volume most teams never touch. Levelling: the posting says this team hires across all levels and that candidates are individually assessed and levelled by skill and experience, which is why the title spans Staff and Senior Staff. Location and working pattern: Bengaluru. Netskope describes open desk office layouts, so expect an in office team. The number of days is not stated. Application timing: the posting states the application window is expected to close within a set period, so do not sit on it. Honest fit guidance: the 6 years specifically in Go or Python backend and distributed systems is the substantive bar. Green field means less structure and more ambiguity than joining an established platform.
What the posting requires as minimum: - 10+ years of experience building software systems, including both user facing products and internal platforms. The preferred qualifications are described by Stripe as a bonus rather than a requirement, and it says explicitly that if you meet the minimum requirements you are encouraged to apply. What the work actually looks like: - Architect and build scalable, reliable systems underpinning Stripe's sales motion, including APIs and services for deal creation and modelling, multi party approval workflows, contract generation and merchant onboarding at global scale. - Lead the single plane of glass vision for sellers, driving architectural decisions on how first party Stripe components and third party platforms such as Salesforce interoperate, and designing systems that surface the right data and actions at the right time. - Integrate AI and agentic workflows into the selling process, from intelligent deal recommendations and pricing guidance to automated contract workflows and proactive activation nudges. - Partner at a senior level with go to market, finance, legal and product teams, translating business requirements into durable technical solutions. - Raise the technical bar through design and code reviews, and establish engineering standards and best practice. - Drive simplification and platform consolidation, reducing complexity in the go to market systems stack and architecting toward a maintainable, extensible platform. - Mentor and sponsor senior engineers, helping them grow in technical scope and organisational influence. How the team describes its own approach: it treats the selling process as an engineering problem from first principles, implemented as a hybrid architecture of first party custom Stripe components interoperating with third party standard business components. Location and working pattern: Bengaluru, India. Office days are not stated in the posting. Honest fit guidance: internal systems at Stripe still run at payments scale, so do not read internal as small. The specific thing that will distinguish an application is experience owning architecture across a boundary between custom systems and large third party platforms, because that hybrid is the core problem the team exists to solve. Ten years is a real bar here rather than a rounded up preference.
What the posting requires: - 12+ years of professional experience in backend systems or platform engineering. - Experience building platforms, infrastructure or database engineering, especially with MySQL and TiDB. - A track record of building and scaling mission critical platforms supporting high throughput global services. - Experience with schema migration frameworks, CI/CD automation and developer tooling. - A champion of developer experience, creating clear, reliable and user friendly workflows. What the work actually looks like: Schema migration platform: - Architect and maintain automated schema change infrastructure for MySQL and TiDB. - Implement versioning, rollback, validation and auditing for safe, predictable and fast migrations. Scale, reliability and test harness: - Ensure the platform operates across thousands of global databases with zero downtime. - Enhance resilience, observability and recovery. - Build a test harness for migration and deployment validation. Developer experience and CI/CD: - Design intuitive tools and APIs for schema and data change management. - Embed migration workflows into CI/CD pipelines to increase velocity and reduce friction. Schema and data management: - Design infrastructure for safe, large scale schema and data operations such as backfills and purging. - Ensure backfill operations are monitored, retryable, minimally disruptive and automated, maintaining data integrity. Why the role matters, in the employer's framing: the platform underpins Databricks' ability to keep evolving rapidly across thousands of databases and more than 60 mission critical services. Location and working pattern: Bengaluru. Office days are not stated. Compliance note in the posting: the standard Databricks export controlled technology clause applies, under which applying for a US government licence is at the employer's discretion where such access is required. Honest fit guidance: this is one of the most specific postings on today's board, which is good news if it matches you and bad news if it does not. MySQL and TiDB experience at scale is named directly, and schema migration tooling is a real specialism. Twelve years is the highest bar in today's edition alongside Samsara's platform role.
What the posting requires as minimum: - A Bachelor's degree in Computer Science or Engineering, or equivalent practical experience. - 12+ years of experience in software design, development and algorithm related solutions, including time in an architect or leadership role. - Stellar programming and coding fundamentals. - 4+ years of experience building large scale distributed systems. - 4+ years of experience leading cross organisation projects. - Expertise working with web services at massive scale deployed on cloud providers such as AWS, GCP or Azure. What an ideal candidate also has, per the posting: - 15+ years of relevant industry experience in software development. - Enjoyment of leading and mentoring other engineers. What the work actually looks like: - Drive technology choices, implement services and establish architectural patterns with broad and lasting impact on Samsara's platform. - Identify opportunities and initiate strategic conversations that influence the direction of platform technology and engineering culture. - Architect, design and develop software that enables Samsara to operate at scale. - Lead teams delivering products and capabilities that contribute to Samsara's products and revenue. - Solve the hardest technical problems and enable products to reach market quickly with high quality. - Keep a platform first approach while collaborating with infrastructure and product teams. The systems in scope, as named: reporting, automations and enterprise capabilities, plus foundational infrastructure including container orchestration, data storage, real time data ingestion and GraphQL execution. Location and working pattern: this one is stated clearly, which is unusual and useful. The role is hybrid at 3 days per week in the office, at Prestige Tech Park, Kadubeesanahalli, Bengaluru 560103. What Samsara says about the environment: engineers have a rare closeness to the end user, with the opportunity to join customer interviews, work with customer success and product managers, and use metrics to check the work is producing better customer outcomes. Honest fit guidance: 12 years with an architect or leadership component is the real bar, and the ideal candidate note pushes it toward 15. This is a technical leadership seat, so the cross organisation project experience matters as much as the coding depth.
What the posting asks for. GitLab publishes no years of experience figure, so judge yourself against the skills: - Strong software development skills with experience in Ruby on Rails. This is stated as required. - Experience working on distributed applications with large codebases deployed in cloud environments, described as strongly preferred. - A passion and desire to proactively develop security engineering skills. Note that formal security engineering experience is explicitly not a hard requirement for this role. - Comfort working in an all remote environment where results and impact matter above hours worked. - Strong experience with cloud native development on Google Cloud Platform and/or AWS. - Interest in thinking like a hacker and defending against attacks with an automation first mindset. - Interest in handling trust and safety security incidents and collaborating with engineering to harden platform defences. - Experience working on an AI native development team maintaining teams of agents, acting as a code reviewer, and abstracting your role away from writing code in most cases. What the work actually looks like: - Maintain core abuse prevention systems and build new abuse detection rules for evolving platform abuse including spam, AI and token abuse, SEO optimisation and redirects, and other financially motivated campaigns. - Become a core maintainer of the in house abuse platform, a Ruby on Rails monolith, supporting and building new features for it. - Improve and expand agentic AI capabilities in the abuse mitigation tools, including improving multi agent reasoning decision patterns with a target of reducing human in the loop operational load. - Lead collaboration with peer engineering teams to deliver safety improvements to the GitLab product. - Resolve automation gaps and create efficient automated processes. - Create and maintain documentation such as runbooks and procedures. About the team: Trust and Safety is a global team that keeps abusive behaviour off GitLab.com using automation, LLM aided anomaly detection and user behaviour analysis. It works closely with Security Incident Response, Signals Engineering, Threat Intelligence and the Red Team. Location and working pattern: GitLab states that all of its roles are remote, with some carrying location based eligibility requirements its talent acquisition team can clarify. This listing includes India. One more thing worth knowing: GitLab treats AI as a core productivity multiplier and expects team members to use it in daily workflows. Honest fit guidance: this is the clearest route on today's board from software engineering into security engineering, because the posting says so itself. Rails is the non negotiable part.
What the posting asks for. As with GitLab's other listings, no years of experience figure is published: - Strong experience building backend applications with Ruby on Rails in a high scale production environment. - Professional experience with Go for backend or infrastructure oriented services. - A track record of leading architecture across multiple systems and influencing technical direction through strong engineering judgement. - Experience writing clear technical proposals, requests for comments and decision records in an asynchronous, documentation first environment. - A solid security mindset, and comfort working on products where trust, risk reduction and secure defaults are central requirements. - Familiarity with software supply chain security concepts such as build provenance, artifact signing, dependency security or software bills of materials. - Strong teamwork and communication skills across distributed teams and functions. What the work actually looks like: - Define and drive the technical architecture for the supply chain security add on, covering package policy enforcement, provenance generation, artifact signing and malicious package detection. - Lead design and implementation for Supply-chain Levels for Software Artifacts Level 2 and Level 3 capabilities within GitLab CI/CD. - Architect integrations with Sigstore services including Cosign, Fulcio and Rekor, covering signing workflows, verification and trust boundaries. - Design backend services and request paths supporting allow, deny and quarantine package policies with strong performance and reliability expectations. - Review merge requests with a focus on security, architectural consistency, maintainability and test quality. - Mentor backend engineers across experience levels, including participating in hiring. - Partner with Product, Infrastructure, Authentication, Authorization and Security counterparts on cross team decisions. - Contribute to open source and industry conversations, including working groups on software supply chain security. Example projects named: a Dependency Firewall for package policy enforcement across supported registries, and artifact attestation and signing using supply chain security standards and the Sigstore ecosystem. Location and working pattern: GitLab states all of its roles are remote, with some carrying location based eligibility requirements. This listing includes India, and the posting describes the environment as remote, asynchronous and values driven, where written communication and ownership are central. Honest fit guidance: this is a genuine architecture role, so the distinguishing requirement is having defined systems before they were built and written the proposals that got others to agree. Rails at high scale plus real Go experience is the technical floor, and supply chain security familiarity is what will separate applications.
What the posting asks for. GitLab publishes no years of experience figure: - Experience owning product or platform systems end to end across multiple engineering and product teams at the staff level. - Strong fullstack technical skills with proficiency in Go, Ruby and Node. - Depth in at least one of: reporting and metrics platforms, graph data systems, or data access and warehouse integrations including Snowflake, Databricks or BigQuery. - Hands on experience building production data ingestion, systems integrations or data access capabilities. - Experience operating multi tenant systems across both SaaS and self managed environments. - Strong judgement in ambiguity, with the ability to make trade offs, create clarity and move early stage work forward. - Clear written communication, including architecture proposals and design records that help distributed teams progress asynchronously. - A practical and proactive approach to collaboration. The posting welcomes transferable experience from adjacent platform, data or product engineering backgrounds. What the work actually looks like: - Architect how GitLab and third party data is ingested, modelled and synchronised into the Knowledge Graph as a near real time graph of the development ecosystem. - Coordinate integration with external systems such as Jira, observability tools, Zendesk and ServiceNow to add business context. - Design the Data Marketplace so customers can consume GitLab data through Snowflake, Databricks and BigQuery without engineering handoffs. - Publish GitLab observability data as OpenTelemetry and support the APIs used for data access. - Define operational readiness for the systems your team ships across GitLab.com, Dedicated and Self Managed deployments. - Translate ambiguous product problems into practical technical plans and iterative roadmaps with Product, Design and the graph backend team. - Resolve cross team coordination with Graph Backend, AI Platform and Infrastructure. - Mentor senior and intermediate engineers through design reviews, pairing and code review. About the team: Data Products covers Software Engineering Intelligence, dashboard and reporting frameworks, external data access and output, and data ingestion across areas such as DORA metrics, value stream reporting, AI impact, software delivery trends, and security and quality metrics. Location and working pattern: all GitLab roles are remote, with some carrying location based eligibility requirements. This listing includes India. Honest fit guidance: the 0 to 1 framing is the main thing to weigh. If you want a defined roadmap this is the wrong role, and if you want to make the early calls it is unusually attractive. The multi tenant across SaaS and self managed requirement is the technical detail most candidates underestimate.
What the posting lists as must haves. GitLab publishes no years of experience figure, though it does specify senior level: - India based and authorised to work in India, specifically Bangalore, Mumbai or Delhi. - Senior level technical pre-sales, solutions architect or sales engineering experience, able to guide stakeholders through solution design and evaluation. - Proficiency across the end to end software development lifecycle, modern DevSecOps practices and CI/CD workflows, with hands on experience with GitLab or a comparable source control, CI/CD or DevSecOps platform, and the ability to map platform capabilities to customer use cases. - Demonstrated experience working with or in leadership roles inside India based global system integrators, either partner facing or on the delivery and practice side. The posting itself calls this the line that makes it a GSI hire rather than a generic solutions architect one. - Channel, cloud and partner ecosystem fluency: understanding partner sourced motion, deal registration, co-sell and services led go to market, rather than a direct sales only background. - Ability to communicate credibly from practitioner to executive, and to own a technical activation plan as the directly responsible individual. - Willingness to use AI as a core productivity multiplier in daily workflow. Nice to haves: - An existing network inside one or more of the named global system integrators. - Experience standing up partner delivery practices, enablement tracks or certification programmes. - Cloud marketplace or hyperscaler co-sell familiarity with AWS or GCP. - Depth in GitLab, AI development, Kubernetes, infrastructure as code, or application security including SAST and DAST. - Coverage value beyond India, such as APAC GSI delivery hubs. What the work actually looks like: - Work with Ecosystem Sales Managers and partner sales leaders on partner activation plans that drive partner sourced pipeline and GitLab adoption. - Own the technical side of partner activation, building and delivering demos, reference architectures and workshops. - Serve as the directly responsible individual on partner technical activation plans, identifying and addressing technical risks and gaps. - Build relationships with technical and business stakeholders at partners so they move from awareness to leading deal registrations and solution development. - Act as partner advocate internally, sharing field feedback with Product, Engineering, Sales, Marketing and Partner Enablement. Location and working pattern: remote, with the posting requiring you to be India based in Bangalore, Mumbai or Delhi. Honest fit guidance: the GSI requirement is the gate. Strong solutions architecture experience without system integrator exposure will not clear it, because the posting says so directly. If you have worked inside an Indian GSI practice and can hold a technical room, this is a rare remote fit.
What the posting requires. Twilio publishes no years of experience figure here, so measure yourself against the skills: - A Bachelor's or Master's degree in Computer Science, Engineering or a related field. - Hands on experience with high scale messaging and streaming systems and processing engines, at several thousand events per second. - Excellent high level service design skills: modelling interfaces and internals in a properly modular, composable and extensible way. - Expert programming experience in Go or a similar object oriented language. - Demonstrated experience identifying, prototyping and deploying GenAI or LLM solutions to solve real world engineering or data processing challenges. - Technical depth in distributed systems, high scale ingestion and cloud native architecture. - Excellent communication, problem solving and operational excellence skills. Desired rather than required: - Proficiency with datastores, distributed caching and container technologies such as Docker and Kubernetes. - Experience with modern CI/CD pipelines and observability tools such as Datadog, Grafana and Prometheus. - Familiarity with AI and ML technologies. - Experience managing and operating AWS infrastructure. - Experience working in a geographically distributed team. What the work actually looks like: - Own and deliver robust, high scale routing experiences for the Twilio Segment data platform and pipelines team. - Architect and operate always available, complex distributed systems in cloud environments. - Guide technical decisions, articulating trade offs between cost, performance and reliability. - Translate complex technical strategies into actionable plans for stakeholders including product managers and architects. ⚠️ Location, and this one has a real restriction: the role is remote but based in India **only in Karnataka, Tamil Nadu, Maharashtra, Telangana and Delhi**. If you live outside those five states you are not eligible, which is unusual and easy to miss. The posting also says you may be required to travel occasionally for project or team meetings. Benefits stated in the posting: competitive pay, generous time off, parental and wellness leave, healthcare and a retirement savings programme, varying by location. Honest fit guidance: Twilio says it values non traditional career paths and encourages anyone meeting the required qualifications to apply. That said, Principal at Twilio is a senior individual contributor level and the scale described, several hundred thousand data points per second, is the real context. The posting also notes Twilio uses AI in its hiring process, with every hiring decision made by a person.