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Added July 22, 2026
What the posting asks for: a degree in a highly analytical field such as Engineering, Mathematics or Computer Science, and up to 2 years of experience with Python and shell or Bash scripting. You need working Linux and command line skills, hands on experience with SQL and NoSQL databases, strong problem solving and analytical ability, clear communication, and the temperament to stay calm and methodical while production is broken. Nice to have, not required: familiarity with monitoring and alerting stacks such as Prometheus, Grafana or ELK, exposure to Kafka and Redis, experience with CI/CD pipelines and deployment automation, and any prior time in a support, SRE or production engineering role. The real day to day: monitoring production services and trading systems, responding fast to alerts, incidents and outages, triaging across trading support services, databases and infrastructure, escalating to the right owners, and driving fixes to completion. You will also build automation to cut repeated manual work, improve observability by extending monitoring, logging and alerting, and maintain operational runbooks and SLO and SLA metrics. Location and setup: Gurugram, Haryana, on site. Graviton describes an open plan office with no cubicles, catered breakfast and lunch, a stocked kitchen, 6 weeks of paid vacation and an international team outing. Honest fit guidance: read the phrase "up to 2 years" as an upper bound, not a floor, which makes this reachable straight out of college if your Python, Linux and database fundamentals are solid. It suits someone who likes operational work and debugging live systems more than greenfield feature building. Trading support means production pressure and incident response are the job, not an occasional interruption. The posting does not publish an interview process.
What the posting asks for: a Bachelor's degree in computer science or a related field, proficiency in Linux and C or C++, and strong problem solving with the ability to work independently. Netradyne states the experience range as 1 to 3 years. Nice to have, not required: familiarity with Agile development methodologies and an understanding of Python. The real day to day: designing and implementing software solutions for the IoT platform, turning functional and technical requirements into detailed architecture and design, building scalable and high performing systems, architecting data driven solutions, writing documented and testable code, troubleshooting and debugging complex production issues, and applying software development best practices. Location and setup: Netradyne states this explicitly, which is unusually clear. Employment type is full time, the work model is on site, and the shift is general shift, 5 days a week. The office is HM Tech Park, Whitefield, Bangalore, and the posting notes potential for some work flexibility. Treat this as a 5 day in office role in Whitefield unless the recruiter tells you otherwise. Honest fit guidance: this is one of the few roles on today's board that a first job engineer can actually clear, but the C and C++ requirement is real and the work is embedded, not web or application development. If your experience is in Java or Python web stacks, the title will fit and the skills will not. Netradyne also publishes a recruitment fraud warning: official communication comes only from addresses ending in netradyne.com or us-greenhouse-mail.io, the company never asks for payment for equipment, and it does not use agencies that charge candidates a fee. The posting does not publish an interview process.
What the posting asks for: 1 to 3 years of L2 technical support experience with enterprise SaaS platforms in a customer facing role, AWS cloud preferred. Mindtickle also wants a track record handling technical issues in production while supporting distributed systems, experience supporting a multi tenant SaaS product including its integrations and configuration management, and hands on troubleshooting of AI powered features with the ability to separate model issues from data, configuration or system level failures. Knowledge of authentication and enterprise integrations is required, specifically SSO protocols such as SAML, OAuth 2.0 and OpenID Connect. The real day to day, in three parts. Operational support: responding quickly to customer queries, providing workarounds while permanent fixes are built, owning tickets to SLA, acting as an escalation point, and working across Product, Engineering, Quality Engineering and DevOps to kill recurring issues. Technical investigation: reproducing and debugging problems across backend services, APIs, data flows, UI behaviour and AI components, capturing browser logs, HAR files and network traces, analysing AI decision paths and validating model outputs for drift or anomalies, and running structured impact and severity analysis before escalating. The named tooling is Zipy, Datadog, SumoLogic, FullStory, Mixpanel and Metabase. Knowledge work: building runbooks, playbooks and troubleshooting guides, converting solved issues into product and documentation improvements, and running deep dive sessions and post incident walkthroughs. Automation: writing Bash, Python and SQL to automate log extraction, data analysis and issue replication, and contributing to AI assisted troubleshooting tooling. Location and setup: Pune, Maharashtra. The posting does not state office days. Honest fit guidance: this is support engineering, not product engineering, so you will be measured on resolution and SLA rather than shipped features. In exchange you get real depth in distributed systems debugging and unusually direct access to Engineering and Product. Mindtickle itself frames it as an L2 role with more influence than most. Good fit if you enjoy diagnosis and customer contact, poor fit if you want to be writing product code most of the week. The posting does not publish an interview process.
What the posting asks for: 2 to 5 years of professional software engineering experience, experience with a modern programming language such as Python, Go or Rust, excellent communication, and comfort with modern infrastructure and delivery systems including containerised applications, Kubernetes and CI/CD tooling such as Drone.io or similar. MongoDB describes the ideal candidate as collaborative, detail oriented and genuinely interested in making software usable. Nice to have, called the bonus round in the posting: experience building full stack applications from front end UIs through backend API handlers to database migrations, and knowledge of Next.js, FastAPI or React. The real day to day: building and maintaining internal tools and platforms that improve workflows and efficiency, writing clean and maintainable code to fix bugs and add features, and working with other engineers and teams to prioritise and deliver. MongoDB publishes its success measures for the role, which is rare and worth reading as the actual expectation. Within three months you should be familiar with internal platforms and workflows and contributing meaningfully to projects in flight, and should have reduced manual friction in internal processes by delivering working features or improvements. Within six months you should have implemented tooling or enhancements that measurably improve internal engineering efficiency and productivity. Location and setup: Gurugram, and the posting is explicit that MongoDB is looking for candidates based in Gurugram for its hybrid working model. Honest fit guidance: internal tooling is sometimes treated as lower status than customer facing product work, and it is worth being clear eyed that your users are colleagues rather than customers. The upside is a new team, early influence over how it works, and a short path from writing something to seeing it used. If you want to work on the MongoDB database engine, this is not that role.
What the posting asks for: 2 to 5 years as a Software Engineer or Python Developer, strong Python with the ability to pick up new frameworks and libraries quickly, hands on SQL for querying and data manipulation, experience with data visualisation and plotting tools such as Matplotlib, Seaborn or Plotly, familiarity with alerting and monitoring tools, and a solid grounding in data structures, algorithms and object oriented programming. Graviton also asks for strong analytical thinking and attention to detail. The real day to day: designing and developing efficient Python scripts for data analysis and automation, building accurate data visualisations that support decision making, implementing and maintaining alerting tools that monitor data workflows and systems, working with other teams to gather requirements, and continuously optimising data processing pipelines for scalability and performance. Location and setup: Gurugram, Haryana, on site. Graviton describes a cubicle free office and a deliberately non hierarchical culture. Published benefits are competitive compensation, 6 weeks of paid vacation, catered breakfast and lunch, a fully stocked kitchen, monthly after work parties and an international team outing. Honest fit guidance: this is a data and tooling engineering role inside a trading firm, not a quant researcher or trader seat, so do not apply expecting to build alpha models. What you get instead is a high signal engineering environment, exposure to financial market data at scale, and a bar set by a firm that says openly it insists on hiring the very best. Graviton has three separate openings on today's board (this one, HPC Engineer at 2 to 3 years, and Application Reliability Engineer at up to 2 years), all in Gurugram and all distinct teams, so compare the three before applying rather than sending the same application to each. The posting does not publish an interview process.
What the posting asks for: Netradyne states the experience range as 2 to 5 years, and separately asks for 2 to 4 years in SDET or quality engineering roles. It publishes a skills table: Python at expert level and shell scripting in Bash or Zsh at intermediate or above are required, while SQL and PostgreSQL at intermediate level and Jenkins or similar CI/CD tooling at intermediate level are listed as desired. The real day to day: designing and executing end to end test plans that validate hardware and software modules of IoT devices, including firmware behaviour, sensor integrations and communication interfaces. Developing and maintaining automated test scripts that verify device function across real operating conditions, edge cases and failure scenarios. Building tooling that simulates real world device conditions so resilience can be verified without relying solely on physical test environments. Monitoring and querying large scale device datasets and telemetry logs to detect anomalies, regressions and performance degradation. Contributing to the architecture of the in house test automation framework so it stays extensible and reusable across teams. Integrating automated tests into CI/CD pipelines for fast feedback on every build. Identifying and triaging defects with clear reproduction steps and root cause analysis. Championing quality practices including code review, coverage standards and shift left testing. You would work directly with hardware engineers, firmware developers, backend engineers and product teams, on the Engineering Quality team in Bangalore. Location and setup: Bangalore. This posting does not state office days, though Netradyne's other Bangalore openings specify on site, general shift, 5 days a week at HM Tech Park, Whitefield, so ask. Honest fit guidance: the Senior label and the 2 to 5 year range are a good illustration of why the years matter more than the title. This is hands on test engineering with real hardware in the loop, which is a different skill set from pure web QA automation. If you want to move from manual or UI testing into device and telemetry work, it is a strong bridge. The posting does not publish an interview process.
What the posting asks for: the ideal candidate has 2 to 3 years of experience, with a degree in EE, CS or CSE. Graviton adds that Bachelor's graduates will also be considered given an exceptional background and prior experience in the HPC field. Required skills are programming in Python or Bash, a good working understanding of Linux at user level, experience with large scale distributed HPC systems, and an understanding of large scale batch processing systems such as PBS Pro, Torque or Slurm along with approaches to efficient scheduling. HPC system monitoring and automation tools are preferred. The responsibilities as listed: hands on experience deploying high performance clusters, working with cluster management tools, exposure to multiprocessor and multithreading concepts, working on Linux, and handling testing, user support, troubleshooting and root cause analysis of issues. Experience with parallel filesystem environments is marked optional. Location and setup: Gurugram, on site. Published benefits are competitive compensation, an annual international team outing, fully covered commuting expenses, health insurance, catered breakfast and lunch, a stocked kitchen, 4 weeks of annual leave plus market holidays, gym and sports club memberships, and regular social events. Honest fit guidance: this is infrastructure and systems work, not trading or model development, and the day to day is closer to a research computing role at a university or national lab than to a software product team. That makes it an unusual and quite specific opening. The explicit note that strong Bachelor's graduates will be considered is worth taking seriously if you have real cluster experience from an academic or internship setting, since HPC exposure is genuinely hard to come by in India outside a handful of employers. Note this is Graviton's third open role today alongside a Python engineer role and an application reliability role. The posting does not publish an interview process.
What the posting asks for: a Bachelor's or Master's degree in Computer Science or a related field, and 3 to 6 years of Python based data or backend engineering. Required alongside that are strong programming fundamentals in data structures, algorithms and object oriented design, experience with AWS services such as Kinesis, SQS or Lambda, experience building and managing data pipelines and workflows with Airflow DAGs, a good understanding of data modelling and both relational and NoSQL databases, experience with Docker and Kubernetes, hands on GitHub and CI/CD tooling such as Jenkins, and strong Linux fundamentals and debugging skills. Preferred, not required: prior experience at a product or SaaS company, streaming and event driven architectures, exposure to NoSQL stores such as MongoDB or Cassandra, a basic understanding of ML pipelines, and familiarity with modern AI concepts such as RAG, AI agents and prompting. The real day to day: building and maintaining scalable pipelines for real time and batch processing on Kinesis and SQS based systems, developing data platforms that support ML feature generation, training and validation, optimising pipeline performance and reliability at high volume, writing backend components and services for data processing, deploying and troubleshooting production data systems, working with distributed systems in Docker and Kubernetes on EKS, partnering with Data Science and ML teams to productionise AI solutions, and following engineering practices around code quality, testing and CI/CD. The posting also lists working with AI agents to solve data engineering problems. Location and setup: Bangalore. This posting does not state office days, but Netradyne's other Bangalore roles specify on site, general shift, 5 days a week at HM Tech Park, Whitefield, so assume in office and confirm. Honest fit guidance: strong choice if you want data engineering that stays close to production ML rather than to BI and reporting, and if you are comfortable that a good share of the week is backend service work and on call style troubleshooting. Netradyne has three separate openings on today's list across different teams, so pick the one that matches rather than applying to all three.
What the posting asks for: extensive and expert programming experience in at least one general purpose language such as Java, C or C plus plus, and the ability to write maintainable, scalable, unit tested code. Also required are experience with multithreading and concurrency programming, experience building highly scalable business applications involving complex business flows and large data volumes, strong object oriented design skills with knowledge of design patterns and a genuine interest in designing clean module and class level interfaces, excellent coding ability so a design converts fluently into code, knowledge of test driven development, a good understanding of databases such as MySQL and NoSQL stores such as HBase, Elasticsearch and Aerospike, and full lifecycle development experience on Linux. PhonePe also asks for experience in a startup environment with high levels of ownership. The title states 3 to 5 years. The real day to day: building robust and scalable web based applications, building abstractions and contracts with separation of concerns at larger scope, driving problem solving for high level business and technical problems, doing high level design with guidance including functional modelling and module breakdown, making incremental architecture changes and analysing their impact, performance tuning large scale distributed systems, working closely with a Product Manager to turn features into capability views and leading execution of medium sized projects, tracking the impact of what ships and iterating, and mentoring junior engineers while breaking execution into phases to make delivery predictable. Location and setup: Pune. The posting does not state office days. Honest fit guidance: PhonePe describes a culture where you own your work end to end from day one and where teams often build frameworks from scratch, which is a real signal about pace and expectations. The payments domain means correctness and availability matter more than shipping speed, and the volumes quoted are not marketing: 330 million transactions a day is a genuinely demanding backend environment. Note this is the Pune opening, and PhonePe also has a Bangalore SRE role on today's board, which is a different team and a different skill set.
What the posting asks for: 3 or more years in a DevOps, SRE or Infrastructure Engineering role. Sarvam marks two requirements non negotiable: strong hands on production Kubernetes covering deployments, Helm charts, HPAs, CronJobs, node affinity, resource management and debugging pod failures, and proficiency with at least one major cloud among Azure, GCP or AWS covering managed Kubernetes, container registries, blob storage, secrets vaults and IAM. Beyond those it asks for experience building and maintaining CI/CD pipelines with container builds, automated testing gates, multi environment promotion and deployment automation, solid containerisation including production Dockerfiles, multi stage builds and image optimisation, experience with monitoring and observability stacks such as Prometheus, Grafana, OpenTelemetry or Sentry, strong Linux systems knowledge across networking, process management, storage and system level debugging, scripting in Python or Bash, a good understanding of networking including DNS, load balancing, ingress controllers, TLS termination and CDN configuration, experience with secrets management patterns such as External Secrets Operator or Sealed Secrets, and familiarity with Redis or similar in memory stores for queues and pub sub. The real day to day: operating production Kubernetes with multi role deployments across API servers, task schedulers, per stage workers and WebSocket servers. Building and optimising CI/CD with staged rollouts across QA, staging and production. Maintaining Helm charts, shared module dependencies and environment specific value overlays. Implementing metrics, dashboards, error tracking and distributed tracing. Operating blob storage with CDN for media delivery, ingress, secrets and IAM. Coordinating multi cloud deployments and artifact management for Docker images and internal Python packages. Running vault to cluster secret sync. Optimising async task queues including dead letter handling and stuck message recovery. Monitoring database query health, connection pooling and backup and recovery. Building developer productivity tooling and local dev environments. Owning incident response including runbooks, alerting rules and post mortems. Driving cost optimisation through right sizing, autoscaling policies and storage lifecycle management. Location and setup: Bengaluru. The posting does not state office days. Honest fit guidance: the workload is ML heavy and GPU backed, which is meaningfully harder to operate than a standard web stack, and the posting is honest that failures cascade across pipeline stages. Good fit if you want infrastructure work at a company where the infrastructure is the hard part. Sarvam also has a Studio backend role open today at 4 to 6 years, which is the same platform from the application side.
What the posting asks for, listed by Zscaler as minimum qualifications: demonstrated curiosity and active use of AI tools with a history of integrating new technology into daily workflows, 3 or more years of Java in a highly distributed and enterprise scale environment with experience in Kafka, Druid and Elasticsearch, experience being on call and handling cloud incidents including writing root cause analyses, working knowledge of cloud infrastructure services on AWS or Azure, and a Bachelor's or Master's in Computer Science or equivalent experience. Preferred qualifications, described as what will make you stand out: experience building full CI/CD systems using Kubernetes and web service frameworks, experience building reliable and extensible data tiers for large scale web services with Postgres and Redis, and experience with identity and access management systems such as Okta, SAML protocols and OAuth. The real day to day: understanding how to build and operate high scale systems, providing service and product wide architectural guidance and driving technical decisions, establishing and enforcing best practices for coding, testing, observability and CI/CD to keep services production ready, and driving cross team collaboration on performance optimisation, reliability improvements and adoption of new technology to accelerate feature velocity. Zscaler also publishes a success profile: comfort with ambiguity and building the path as you walk it, acting like an owner with a bias for action, running toward hard problems, and being a high trust collaborator who puts team success first. Location and setup: the posting lists the location as India and is tagged hybrid, reporting to a Senior Director of Software Engineering. The location field on this posting reads "India, Update Location", so confirm the specific city with the recruiter before applying. Honest fit guidance: the mismatch between the Senior title and the 3 year minimum is worth acting on, because engineers at 3 to 4 years often filter out anything labelled Senior and would never see this. The AI tooling requirement is listed first among the minimum qualifications, not as a nice to have, which tells you something about how the team expects to work. Being on call is explicitly part of the job.
What the posting asks for: 3 or more years of industry experience building large scale NLP and NLU systems, with recent hands on work in LLMs and LLM powered paradigms including agentic AI. The real day to day: designing and developing state of the art LLM powered AI capabilities and agentic or multi agent systems end to end, from idea through to production, in a fast moving startup environment. Working with current tooling across machine learning, deep learning and natural language processing. Building and maintaining highly scalable production systems that power the AI capabilities in the product. Optimising ML models and processing pipelines for performance, cost effectiveness and scale. Mentoring more junior engineers and influencing peers and stakeholders. Collaborating across engineering, product, customer facing teams and customers. What the team offers beyond the work: Observe.AI states that you will have the opportunity to publish at top conferences and to influence the company's product and platform strategy, and describes the ML team as working on multi agent systems, LLMs and applied research turned into production systems handling millions of interactions daily. Location and setup: Bengaluru. The posting does not state office days. Honest fit guidance: the posting says plainly that it wants someone who is "truly an engineer at heart", and the responsibilities back that up: this is applied ML with a heavy production systems component, not a research seat. If your experience is fine tuning and evaluation without owning the serving path, expect the scale and reliability parts to be the stretch. At 3 or more years it is one of the more accessible senior ML openings on the board, and it sits in the same company as an SDE III backend role also listed today, which is a different team.
What the posting asks for: Twilio states it is hiring for multiple positions at either a Senior or a Staff level under this one listing. The Senior level asks for 3 or more years of professional software development experience, ideally full stack. The Staff level asks for 5 or more years of professional full stack software development experience. Apply against the level that matches you rather than assuming the higher bar applies. The real day to day differs by level, and Twilio spells both out. At the Senior level: developing, testing and deploying code for prototypes and new product features, working with the team to solve technical problems and improve solution design, taking part in code reviews, documentation and knowledge sharing, experimenting with modern tools, frameworks and emerging technology, and adapting to changing requirements while contributing to process improvement. Twilio calls this a growth role, suited to someone eager to learn in a fast paced and entrepreneurial environment. At the Staff level: designing, developing and implementing prototypes and production ready solutions, experimenting with AI, machine learning and emerging technology alongside the team, contributing to engineering best practice through code review, communicating progress and technical concepts to stakeholders, and adapting quickly to feedback and changing requirements. Location and setup: remote within India. Twilio describes a dedication to remote first work and a culture of global connection, and this is genuinely a distributed role rather than a hybrid one. Two things worth knowing before you apply. Twilio states openly that it uses AI to help make its hiring process efficient, while adding that every hiring decision is made by a person. And Twilio's remote India postings have historically been restricted to a subset of Indian states, so confirm your state is eligible with the recruiter rather than assuming all of India qualifies. Honest fit guidance: incubation work means ambiguity, short cycles and a real chance that something you build does not ship, which suits some engineers and frustrates others. The compensating benefit is breadth and exposure to new technology early. Twilio also has a Staff Business Intelligence Engineer role open remotely today at 7 or more years, which is a different team on the data side.
What the posting asks for: a Bachelor's degree in Computer Science, Engineering or a related field, 3 or more years working with PySpark and SQL, 2 or more years building and maintaining data pipelines on Amazon EMR or Amazon Glue, 2 or more years of data modelling and end user querying with Amazon Redshift or Snowflake plus Athena and Presto, and 1 or more years building and maintaining pipeline orchestration with Airflow. It also asks for strong problem solving and troubleshooting, excellent communication and collaboration, and the ability to work both independently and in a team. Preferred, not required: an AWS Data Analytics Specialty certification and experience with Agile development. The real day to day: building and maintaining production data pipelines on EMR or Glue, designing data models and end user querying on Redshift or Snowflake, Athena and Presto, building pipeline orchestration with Airflow, working with customer and internal teams to understand data needs and design solutions, troubleshooting and optimising pipelines and models until they hold under real load, writing and maintaining PySpark and SQL for extract, transform and load work, documenting and communicating technical decisions to both technical and non technical audiences since customers sign off on what ships, and tracking new AWS data technology to judge whether it belongs in the systems Mactores runs. Location and setup: the listing is tagged Mumbai, Maharashtra, but the posting body states plainly that the role is fully remote. It also describes a forward deployed model where senior engineers embed with the customer's team and carry the delivery commitment personally against fixed dates, with Mactores absorbing overage cost for delays inside its control. Confirm the remote arrangement and any client site travel expectation with the recruiter, since embedded consulting and fully remote can pull in different directions. Honest fit guidance: this is consulting delivery work, so you will be working against customer commitments and sign offs rather than an internal roadmap, and the posting is candid that the culture is measured by what ships. Note also that Mactores lists a separate freelancer variant of this same role, so check which one you are applying to. The 3 year floor against a Senior title makes this reachable earlier than the name suggests.
What the posting asks for: 4 to 6 years in backend engineering with a focus on building and operating production services at scale. Sarvam marks Python and production FastAPI or similar async web service experience as non negotiable. Beyond that it asks for a deep understanding of async programming including asyncio, concurrent execution patterns and designing for high throughput, experience with distributed task systems covering task queues such as Celery, message brokers and fault tolerant job orchestration, hands on PostgreSQL with an async ORM such as SQLAlchemy including query optimisation, schema design and migrations, familiarity with audio and media processing using FFmpeg, common audio formats and libraries such as soundfile or librosa, experience integrating ML models into production whether API based over REST or gRPC or local inference with PyTorch or ONNX Runtime, experience building reusable libraries or SDKs with clean APIs and backward compatibility, proficiency with Docker, Kubernetes, Helm and at least one major cloud, and strong testing discipline including mocking external services and maintaining CI/CD. Bonus points: prior experience with speech or NLP systems such as ASR, TTS or machine translation. The real day to day, across three areas. Service and infrastructure: designing production FastAPI services for dubbing and live translation with multi stage task orchestration, rate limited scheduling and backpressure controls, building distributed worker architectures with independent scaling per pipeline stage and automatic recovery of stuck tasks, owning the async ORM data layer and migrations on PostgreSQL, implementing WebSocket based job tracking and streaming audio pipelines, and managing Kubernetes deployments including Helm charts, secrets, ingress and multi role images. ML pipeline and library: extending the core dubbing library across audio extraction, voice activity detection, speech recognition, translation, quality control, text to speech and video stitching, integrating and optimising ML model serving for both remote inference and local inference, building quality control orchestration including automated scoring, tempo analysis and pronunciation verification, and maintaining LLM integration layers across multiple provider backends. Platform SDK: maintaining the shared Studio SDK with reusable FastAPI middleware and routers for authentication, billing, workspace isolation and input validation. Location and setup: Bengaluru. The posting does not state office days. Honest fit guidance: the media and audio processing element is a genuine specialisation, and the posting is explicit that FastAPI and async depth are not negotiable. If your backend experience is synchronous request and response web services, this will be a real jump. Sarvam also has a Studio DevOps role open today at 3 or more years, which is the same platform from the infrastructure side.
What the posting asks for: a minimum of 4 to 7 years of strong hands on Linux and Unix system administration, including TCP/IP, DNS and load balancers, plus expertise in managing and scaling infrastructure. The real day to day: troubleshooting issues across the entire stack covering hardware, software, application and network. Improving the reliability and performance of distributed systems and containerised deployments. Diagnosing complex distributed systems handling millions of queries per second. Working in a heavily command line driven environment that requires strong Linux understanding. Participating in an on call rotation. Designing, building and maintaining the core infrastructure that lets PhonePe scale to hundreds of thousands of concurrent users. Taking part in analysis and system improvement planning. Driving performance testing, capacity planning and high availability practices. Owning implementations of new technology with proper testing and documentation. Proactively monitoring and solving issues that could affect infrastructure. Onboarding new team members and getting them production ready. Location and setup: Bangalore. The posting does not state office days. Honest fit guidance: the on call rotation is stated in the posting rather than buried, and in a payments business an incident at 2am is a real incident, so weigh that honestly. PhonePe describes a culture where you own your work end to end from day one and teams frequently build frameworks from scratch, which suggests genuine autonomy but also limited hand holding. Worth noting PhonePe has several similarly titled SRE openings live, including SRE 3 and a big data focused SRE at 7 or more years, so check you are applying to the level that fits. This is the Bangalore infrastructure role; PhonePe's Pune backend role on today's board is a different team and skill set.
What the posting asks for: 4 or more years of professional experience in engineering or technical consulting. The role is built on deep Kubernetes knowledge, since Nirmata expects you to go deep in Kubernetes and in Nirmata's own solutions, including using its AI capabilities against customer problems. The real day to day, as Nirmata lists it. Custom engineering and proof of value: building working solutions against live production systems to demonstrate provable return on investment tied to the customer's own data and environment. Cloud native and AI expertise: being the expert who goes deep on Kubernetes as the orchestrator for both software and AI workloads. Enterprise implementation: verifying and implementing detailed technical designs that address complex customer problems and drive platform adoption. Technical leadership: leading customer initiatives by understanding the business implications and architecting for operational, integration and scaling requirements. Operational excellence: providing design and operational solutions for enterprise container adoption, including building the necessary tools and automation. Research and development feedback: gathering field insight and giving Engineering clear product feedback grounded in real customer requirements. Customer advocacy: managing technical escalations, educating customers and documenting best practice. Value realisation: helping customers define, capture and document the financial value the platform delivers. Location and setup: Bangalore. The posting does not state office days. Honest fit guidance: Nirmata says outright that this is unlike a traditional customer success role, and the responsibilities support that, since you are building against live production systems rather than running adoption checklists. The trade off is that your work is measured in customer outcomes and renewals rather than shipped features, and you will spend real time in meetings and escalations. Good fit if you have Kubernetes depth and want to use it in front of customers. Poor fit if you want to be heads down in a codebase. Nirmata is also a much smaller company than most on this list, which usually means broader scope and less structure.
What the posting asks for: 5 to 9 years of experience building large scale products. The real day to day: contributing to building and deploying AI agents for customer service in the form of voice agents, chat agents and copilots at enterprise scale. Owning the full application lifecycle through design, coding, testing, deployment and debugging with a high quality bar. Working with product and design to translate the product roadmap into a clear technical roadmap. Developing reusable code, frameworks and libraries to speed up development and keep projects consistent. Optimising applications for speed, scalability and reliability by proactively addressing bottlenecks and performance issues. Implementing security measures and data protection practices to safeguard applications and sensitive customer data. Designing and managing scalable data storage that stays efficient, reliable and maintainable. Building and scaling efficient data pipelines that process large volumes accurately. Reviewing others' work as part of the team's quality process. Location and setup: Bengaluru. The posting does not state office days. Honest fit guidance: this is backend platform engineering supporting AI products, not machine learning engineering, so the daily work is distributed systems, data pipelines and API design rather than models and evaluation. If you want to move closer to AI without retraining as an ML engineer, that distinction works in your favour. Observe.AI describes a culture that prizes speed and ownership, which usually means less process and more direct responsibility. Note Observe.AI also has a Senior ML Engineer NLP role on today's board at 3 or more years, which is the model side of the same product, so pick according to which half of the system you want to own.
What the posting asks for: 5 or more years of professional experience in machine learning and software engineering. Coinbase states the level directly, hiring an IC5 Machine Learning Engineer. The real day to day: architecting and deploying the orchestration layer that manages state transitions, context sharing and intent routing across vendor and internal LLM frameworks in a distributed conversational environment. Building production grade Python services that connect ML and AI research to reliable, measurable customer facing products. Leading end to end execution on complex ML initiatives, managing priorities, technical trade offs and cross functional dependencies from design through delivery. Establishing best practice for system design, coding standards and AI and ML development workflows across the team. Mentoring engineers on architectural integrity and modern AI and ML patterns. Running design reviews. The team context: CX Intelligence sits inside Coinbase's Enterprise Applications and Architecture organisation and owns the multi agent platform powering Coinbase chat, the Help Center and agent tooling, working with Conversation Design, CX and other engineering teams to deliver secure and compliant AI powered support. Location and setup, and this matters: the role is remote within India, but Coinbase states plainly that it is remote first, not remote only, and that you should expect to gather quarterly for in person working sessions it calls surges. If travel every quarter is a problem, factor that in now rather than at offer stage. Coinbase is also unusually blunt about culture in the posting itself, saying the bar is high and the environment is intense. Honest fit guidance: the interesting engineering here is the hand off logic between different LLM frameworks and between AI and human agents, which is a genuinely hard and current problem. It is applied ML with a heavy production systems and architecture component, so it suits someone who has shipped and operated ML services rather than someone whose experience is model training alone. Coinbase has three remote India roles on today's board across different teams.
What the posting asks for: 5 or more years in IT engineering, automation or systems engineering, with demonstrated ownership of architecture decisions, engineering standards or platform level systems that others build on. Also required are deep expertise building and maintaining complex automations using n8n, Airflow, Temporal, Jira Automation or Workato, paired with strong Git based development workflows including branching, CI/CD and code review, and working knowledge of Okta, Slack API and Google Workspace administration sufficient to design against them. The real day to day: owning the technical architecture of IT Tooling automations and driving design decisions on system structure, integration patterns and tooling across n8n, Airflow, Temporal and Jira. Establishing architecture standards and code review practices that raise the team's engineering bar. Leading as technical decision maker on the most complex, highest impact automation projects from discovery through production. Partnering with Security, Corporate Engineering and Identity and Access Management to clear technical blockers and representing the team in cross functional architecture discussions. Building shared frameworks and reusable components that reduce single points of failure. Producing architecture documentation and decision records so the team scales without depending on one person. Managing your own workload with high independence, turning loosely scoped problems into production ready specifications. Location and setup: remote within India. As with Coinbase's other openings, the company states it is remote first but not remote only, and that you should expect quarterly in person working sessions called surges. The team partners with regional IT groups across Asia Pacific, Europe and the Americas, so some schedule overlap across time zones is likely. Honest fit guidance: be clear about what this is. It is internal IT automation engineering, not product engineering, and your users are Coinbase employees. That is a real career distinction and some engineers will not want it. What it offers in exchange is genuine architectural ownership at 5 years, a modern automation stack, and a remote role at a large employer. If you have been doing IT operations and want to move toward platform engineering, this is a credible bridge. If you want to build customer facing product, look at Coinbase's other two openings on today's board instead.
What the posting asks for: a minimum of 5 or more years of relevant experience as a Data Scientist, preferably in a consulting role. Alongside that Databricks asks for experience building GenAI applications including RAG, multi agent systems, Text2SQL and fine tuning with tools such as HuggingFace, LangChain and DSPy, expertise deploying production grade GenAI applications including evaluation and optimisation, extensive hands on industry data science experience with common tooling such as pandas, scikit learn and PyTorch, experience building production grade machine learning deployments on AWS, Azure or GCP, a graduate degree in a quantitative discipline such as Computer Science, Engineering, Statistics or Operations Research or equivalent practical experience, and the ability to explain technical concepts to both technical and non technical audiences. Preferred, not required: experience using the Databricks Intelligence Platform and Apache Spark to process large scale distributed datasets. The real day to day: developing GenAI solutions that incorporate techniques from Databricks' own Mosaic AI research to solve customer problems. Owning production rollouts of both consumer facing and internal GenAI applications. Serving as a trusted technical advisor to customers across a variety of domains. Presenting at conferences such as the Data and AI Summit and being recognised as a thought leader internally and externally. Collaborating with product and engineering teams to influence priorities and shape the roadmap. Location and setup: the posting states the role can be remote, and the listing specifies India. Honest fit guidance: this is a customer facing professional services role, so a real share of your week goes to customer meetings, discovery and advisory work rather than heads down building. Databricks describes the team as an ensemble that looks for individuals with strong unique specialisations, which suggests depth in one area matters more than breadth. The graduate degree requirement is stated, though Databricks allows equivalent practical experience, so do not self reject on that alone if your production GenAI record is strong. The conference speaking expectation is unusual and worth taking at face value: visibility is part of the job.
What the posting asks for, and read this carefully because the numbers stack: 5 or more years of professional experience overall, and separately 3 or more years working in data engineering, business intelligence or a similar role. The effective floor is 5 years, not 3. Also required are proficiency using AI coding tools such as Claude Code, Codex or Cursor across the full software development lifecycle from design and code generation through testing, monitoring and release, proficiency in Python or Java, 3 or more years with ETL orchestration and workflow management tools such as Airflow, Flink, Oozie or Azkaban on AWS or GCP, expert level database fundamentals, SQL and distributed computing, 3 or more years with the distributed data ecosystem including Spark, Hive, Druid and Presto plus streaming technologies such as Kafka or Flink, experience with Snowflake, Redshift, PostgreSQL or other database platforms, knowledge of reporting tools such as Tableau, Superset and Looker, and strong communication with both technical and non technical teams. The real day to day: working with business partners and stakeholders to understand data requirements, working with engineering, product teams and third parties to collect the required data, designing and implementing large scale high volume high performance data models and pipelines for the data lake and warehouse, developing data quality checks and monitoring routines, improving the reliability and scalability of ETL processes, managing a portfolio of data products that deliver trustworthy data, and helping onboard and support engineers joining the team. Location and setup: Hyderabad, India. The posting states that you must be located near one of DoorDash's engineering hubs, so this is not a remote role despite being on a large distributed company's board. Honest fit guidance: the explicit requirement for proficiency with AI coding tools is worth noting, since it is listed as a requirement rather than a nice to have and names specific products. This is a good example of why reading the experience sentence by hand matters: an automated parser would report this role as 3 years because that number appears three times, when the actual floor is 5. Strong choice if you want data engineering at genuine consumer scale, and DoorDash's data organisation is explicitly the foundation for company decision making rather than a reporting function.
What the posting asks for: 5 to 7 years of experience in a customer account facing role. Datadog also wants a strong communicator with excellent attention to detail, someone motivated by building long lasting relationships, experience negotiating contract terms and overcoming pricing objections without direct guidance for upsell and cross sell, and a proven track record of hitting account expansion targets measured by growth and retention of a portfolio. The real day to day: partnering with the sales team for a smooth transition and onboarding, proactively building customer relationships to create mutual trust, carrying out the full sales cycle for growth opportunities inside existing accounts, advising customers so they use Datadog effectively, monitoring usage trends to uncover renewal risk and promote adoption, and collaborating across Datadog's Support, Product, Finance and Legal teams. What Datadog offers around the role: it publishes a structured onboarding programme and sales training in MEDDIC and Command of the Message, which is a real signal that the methodology is taught rather than assumed. Location and setup: Bangalore, India. Datadog states its position openly, saying it values office culture and operates as a hybrid workplace. Ask what hybrid means in days for this team. Honest fit guidance: be clear about what this role is. Despite sitting on a technical products board, it is a commercial customer success role carrying expansion targets, not an engineering or solutions engineering job. If you want the technical version, Datadog's Technical Account Manager 2 role is also on today's board and asks for hands on cloud, scripting and CI/CD skills instead. Datadog adds a note worth repeating, that not everyone will meet all the qualifications on day one and it encourages applying anyway if you are passionate about the technology.
What the posting asks for: 6 or more years of data engineering experience, with at least 2 of those in a senior or lead capacity. The real day to day: owning and evolving the data platform across StarRocks for OLAP, Apache Hudi for the data lake, Trino, Spark and Apache Ranger, keeping performance, reliability and security intact at scale. Building the next generation AI optimised data layer, meaning clean structured datasets that power natural language querying and AI add on features for HackerRank for Work customers. Owning in product data features including exports, insights dashboards, interview analytics and the self serve custom reports interface. Enabling self service pipelines for internal teams across AI platform, analytics and go to market, which reduces ad hoc data requests. Enforcing data security through access controls, Apache Ranger policies and confidence scoring guardrails on AI generated output. Leading technical design reviews and defining engineering standards for the data team. Partnering with product managers and business stakeholders to identify and scope AI enabled data use cases. Location and setup: hybrid in Bangalore, India. The posting does not state how many days. Honest fit guidance: HackerRank is unusually specific about where the platform stands, which is useful, because it tells you the painful migration is behind you and the work ahead is building features rather than firefighting legacy. The posting calls this a greenfield opportunity to shape the next phase of data at the company. Note the framing as a senior individual contributor at the heart of the data organisation: the Lead in the title means technical leadership and design authority, not people management, so it stays on this board. Good fit if you want platform ownership and direct line of sight from your work to revenue features. Less good if you want a large existing team around you, since the role reads as a small senior group with wide scope.
What the posting asks for: 7 or more years of experience in business intelligence, data analytics or a related field, with at least 2 of those in a staff or senior individual contributor role. The real day to day: owning and evolving Twilio's suite of business intelligence tools, driving governance, performance and long term roadmap decisions. Designing and building scalable, reusable dashboards and reports that surface actionable insight for stakeholders across legal, billing operations and other mission critical functions. Architecting and deploying agentic AI solutions that enable natural language querying, automated insight generation and self service analytics for non technical business partners. Collaborating with data engineers, business partners and AI and ML engineers to turn complex analytical requirements into reliable, well documented data products. Establishing and championing BI best practice including semantic layer design, metric definitions, data access governance and documentation standards. Partnering with business leaders to find opportunities where better data visibility changes decisions. Location and setup: remote within India. Twilio describes a dedication to remote first work and a strong culture of global connection, so this is a genuinely distributed role. One caveat: Twilio's remote India postings have historically been open only to a subset of Indian states, so confirm your state qualifies before you invest in the process. Twilio also states openly that it uses AI to help make its hiring process efficient, while adding that every hiring decision is made by a person. Honest fit guidance: this is a platform and strategy role rather than a report building one, and the agentic AI element is a real part of the brief rather than decoration, so experience with semantic layers and self service analytics architecture matters more than dashboard volume. The stakeholder set is heavily internal business functions including legal and billing operations, which means influence and documentation are as important as technical skill. Twilio also has a remote Senior or Staff Applied Research Software Engineer role on today's board at 3 or more years, which is a very different team.
What the posting asks for: 8 or more years of hands on software engineering experience with a demonstrated track record of the kind of platform ownership this role requires. Coinbase names the level explicitly as Staff. The real day to day: architecting and delivering foundational platform capabilities including tool registries, AI orchestration layers, risk based access automation, and the contribution model that lets product groups across Coinbase build on the platform safely and quickly. Driving the evolution of Control Center into an AI agent ready, headless operations platform by designing procedure constructs, agentic guardrails, human in the loop orchestration and evaluation frameworks so that both people and autonomous agents can carry out sensitive customer operations securely. Partnering with the Engineering Manager and product counterparts to define architectural roadmaps, author technical design documents, lead design reviews, break complex initiatives into deliverable milestones, and raise the engineering bar through code review and mentorship. Building and maintaining highly reliable platform services. Location and setup: remote within India. Coinbase states it is remote first but not remote only, and that you should expect quarterly in person working sessions it calls surges. Coinbase is also direct in the posting about culture, saying the bar is high and the environment is intense. Honest fit guidance: the substance here is unusually current, since designing guardrails and evaluation frameworks so AI agents can safely execute sensitive customer operations is a problem most companies are only starting to think about. The trade off is that this is an internal platform rather than a customer facing product, and the users are Coinbase specialists in compliance, legal and investigations. At Staff level the expectation is architectural leadership and mentorship rather than pure output, so the application should evidence decisions you owned, not just systems you worked on. This is one of three Coinbase remote India roles on today's board.
What the posting asks for: 8 or more years of software engineering experience with significant focus on building production systems, and separately a minimum of 2 or more years of hands on experience with AI integration architecture including using large language models in production workflows. Airbnb frames the ideal candidate as someone who takes ownership of systems and solutions, is eager to learn, and is enthusiastic about applying AI, specifically building new tools and integrations with Claude Code that extend what the team can do. The real day to day, as Airbnb describes a typical day: partnering with Finance and cross functional teams to turn their requirements into integrations, automations and reporting. Working alongside architects and other technical teams in design discussions and reviews. Migrating integrations from Oracle Cloud onto the in house platform. Pairing with and mentoring junior engineers and contractors through design reviews, architecture discussions and pair programming. Triaging and resolving issues across the integrations and infrastructure you support. Exploring the GenAI landscape, evaluating new models and techniques, and sharing what you learn. The stated responsibilities: owning the architecture, delivery and reliability of integrations and microservices in Java, Python and TypeScript, and extending the in house integration platform so technical teams across Airbnb can self serve. Location and setup: the listing reads Remote, Bangalore, India. Confirm with the recruiter whether that means fully remote from anywhere in India or remote while based in Bangalore, since the two are different and the wording does not settle it. Honest fit guidance: this is enterprise and internal platform engineering serving Finance, not consumer product work on the Airbnb marketplace, and that is a real distinction worth being honest with yourself about. What it offers is a large, well engineered internal platform, genuine AI adoption rather than a pilot, and a remote arrangement at a company with a strong engineering reputation. The Finance domain exposure is also a durable specialisation. If you want to work on search, pricing or the booking funnel, this is not that team.
⚠️ Years of experience: GitLab does not state a number anywhere in this posting, and we do not publish figures we cannot source. What GitLab does state is the level range in the title itself, Intermediate through Senior to Staff, so the entry point is the Intermediate rung rather than Staff. GitLab adds that it welcomes interest from candidates with varying levels of experience and that many successful candidates do not meet every requirement. Ask the recruiter where each level sits before you self reject. What the posting asks for: experience building instrumented, observable software systems. Knowledge of Git internals including its code and data structures, or practical experience operating Git servers. Experience writing and testing production code. GitLab also asks that you share its values and work in accordance with them, which it treats as a real requirement rather than boilerplate. The real day to day: taking part in architectural discussions and technical decisions on Git and Gitaly, helping drive implementation choices that improve correctness, performance and maintainability. Contributing features and fixes to upstream Git. Adapting Gitaly to make effective use of new and existing Git capabilities. Representing GitLab as a constructive participant in the open source ecosystem and building relationships that support ongoing collaboration with the Git community. Working across repository access, performance and long term maintainability. Location and setup: fully remote, with India among the listed locations alongside Canada, Poland, the United Kingdom and the United States. GitLab is one of the best known all remote companies, so remote here is the default operating model rather than an accommodation. Honest fit guidance: this is systems programming in C on one of the most widely used pieces of software in the world, and the requirement for Git internals knowledge or Git server operations experience is the real gate. Very few engineers have that, which cuts both ways: the pool is small, so genuine C and Git depth counts for a lot more than years on a CV. The upstream contribution element means your work is public and permanent, which is rare and valuable. Because GitLab spans three levels here, do not rule yourself out on seniority. Note also that a role open across five countries is competitive globally, not just in India.
⚠️ Years of experience: GitLab does not state a number in this posting, and we do not publish figures we cannot source. GitLab does say it welcomes interest from candidates with varying levels of experience and that many successful candidates do not meet every requirement. The framing that you are "eager to grow into customer facing technical leadership with strong mentorship and support" suggests this is not a first job but also not a principal level role. Ask the recruiter directly. What the posting asks for: experience providing technical consultancy and solution guidance through video calls and written channels, helping users or customers successfully adopt technical solutions. Familiarity with DevSecOps workflows and related tooling. The ability to manage multiple customer engagements in parallel, prioritise effectively and communicate status, risks and next steps clearly. An interest in supporting customers across Asia Pacific and Japan and adapting to different cultures, working styles and levels of technical maturity. GitLab states one hard requirement: you must be based in India with full working rights in India. The real day to day: providing on demand technical consultancy and solution guidance over Zoom and in writing, helping customers solve day to day issues and design and implement GitLab solutions for their DevSecOps workflows. Delivering workshops, demos and best practice guidance across the Asia Pacific and Japan region. Working with Account Executives and Renewals Managers to align technical recommendations with customer goals and support renewals and expansion by showing outcomes. Managing multiple engagements in parallel and keeping implementations on track. Contributing documentation and enablement content back to the wider team. Mentoring team members and customers through complex technical challenges. Location and setup: fully remote within India, and GitLab is explicit that this role suits someone who thrives in a remote, asynchronous environment. GitLab is an all remote company, so this is the default way the whole organisation works rather than an exception made for one role. Supporting the Asia Pacific and Japan region means your hours align reasonably well with India, unlike customer roles covering the Americas. Honest fit guidance: this is the technical end of customer success, closer to solutions engineering than to account management, and DevSecOps familiarity is the real requirement. It suits someone who enjoys explaining and unblocking as much as building. The renewals and expansion element means commercial outcomes are part of how the role is judged, so it is not purely technical. GitLab also has a remote backend engineering role on today's board on the Git and Gitaly team.
⚠️ Years of experience: Datadog does not state a number in this posting, and we do not publish figures we cannot source. It does ask for previous experience working as a TAM supporting mid market or enterprise customers in the cloud space, which implies you have done the job before rather than entering it fresh. Ask the recruiter what band the "2" in the title corresponds to. What the posting asks for: a solid technical grounding with hands on IT operations experience. Confident hands on experience with AWS, Azure or GCP. Comfort with scripting languages such as Python, JavaScript, Ruby, Perl, Go or PHP, or configuration tooling such as Chef or Puppet. Skill with CI/CD tools such as Jenkins or GitLab. Knowledge of containerisation tools such as Docker, Kubernetes or Rancher. Previous experience as a TAM supporting mid market or enterprise cloud customers. Experience working across Sales, Support and Engineering. Experience using Datadog or other cloud monitoring tools. One requirement that is easy to miss and hard to undo: the posting asks that you be able to travel by road, rail or air up to 30% of the time. That is roughly one week in three. If travel at that level does not work for you, this is the wrong role regardless of technical fit. The real day to day: advising on technical support and product adoption for customers across pre sales, post sales and renewal processes. Analysing the customer's IT operations environment regularly and recommending changes that maximise the value they get from Datadog. Acting as the customer's advocate by knowing their goals and use cases, then suggesting process changes, product adoption, configuration and additional features. Preparing for and taking part in monthly and quarterly business reviews with customers. Working with Datadog's product management, engineering and technical services teams to help identify new features and products. Continuously evolving best practice for technical product adoption. Location and setup: Bangalore, India. Datadog states it values office culture and operates as a hybrid workplace, so ask what that means in days per week for this team. Honest fit guidance: this is on the board because a reader asked for Technical Account Manager roles specifically, and it is worth saying that the "Manager" in the title refers to managing accounts, not people. The technical requirements are real, so this suits an engineer or IT operations specialist who wants customer facing work rather than a career account manager. Datadog notes that not everyone will meet every qualification on day one and encourages applying anyway. Its Customer Success Manager Key Accounts role is also on today's board and is the commercial counterpart to this one.