What this edition tells you
One thing worth saying plainly about today's batch: there are no entry level roles in it. The lowest bar is Rubrik at 2 years. The 0 to 1 year roles from earlier this week are still live on the board, so use the experience filter rather than assuming today's list is all there is.
Titles mislead in both directions again today. CommerceIQ's "Software Development Engineer II" sounds early career but wants 3 to 6 years, while Zscaler's "Principal" role wants 13. Read the years, not the title.
No roles match that combination.
Added July 21, 2026
What the posting requires - 8+ years in systems or infrastructure software engineering, with a meaningful portion on Kafka or comparable distributed systems such as Pulsar, BookKeeper, ZooKeeper, etcd or CockroachDB - Deep familiarity with the Kafka codebase. The posting draws a sharp line here: it wants people who have read, modified and debugged the broker, controller or client internals, not people who have operated Kafka - A track record of upstream contribution: committed patches, participation in KIP discussions, reviewed pull requests or equivalent engagement with the Apache Kafka community. Committer or PMC status is strongly preferred but not strictly required - Strong distributed systems fundamentals understood as engineering tradeoffs you have navigated: consensus protocols, replication, exactly once semantics, log structured storage and distributed state machines - Systems level programming comfort: JVM internals including garbage collection tuning, memory layout and JIT behaviour; operating system I/O including page cache, fsync semantics and zero copy; and networking including TCP tuning and socket buffers - Experience maintaining a fork or distribution, and managing the tension between carrying local patches and staying mergeable with upstream Counts as a bonus, not a requirement - Apache Kafka committer or PMC membership - Experience with KRaft, the ZooKeeper free controller - Having authored or co-authored a KIP - Kafka Streams or Kafka Connect internals, rather than just usage - Familiarity with Kafka's own test infrastructure including system tests, Trogdor and ducktape What you would actually be doing - Owning the internal Kafka distribution: branches, patch curation, releases and upgrade planning - Contributing upstream to Apache Kafka - Diagnosing and fixing deep broker and controller issues across replication, partition reassignment, KRaft consensus, log storage and protocol handling - Building custom patches for production needs unsuited to upstream - Establishing how the team tests, benchmarks and validates patches before rollout - Mentoring a supporting engineer and growing the team's capability - Advising platform teams on configuration, capacity planning and architecture, and acting as the primary interface for audit and compliance Location and working pattern Bangalore. Staff at PhonePe is a senior individual contributor track. Also worth knowing PhonePe's listed benefits include comprehensive insurance, an onsite medical centre, parental and adoption support, relocation help, provident fund and gratuity, higher education assistance and a car lease policy. A good fit if You have gone deep on infrastructure internals rather than broad across product features, and you want one of the few Indian roles that will pay you to work on an open source distributed system at source level. Think twice if You have operated Kafka but never modified it. The posting makes that distinction explicitly, and the interview will go straight there.
What the posting requires - 3 to 6 years building and operating production backend systems at scale - Expert level in at least one modern backend language such as Python, Ruby, Go, Java or Node.js, with strong fundamentals across the stack - Proven ability to design distributed systems, having made real architectural decisions on service decomposition, data consistency, fault tolerance and observability - Deep expertise with relational databases such as PostgreSQL and MySQL plus NoSQL stores, including schema design, query optimisation and modelling for high throughput - Strong understanding of caching with Redis or Memcached, asynchronous messaging with Kafka or RabbitMQ, and event driven architecture - Hands on containerisation with Docker and Kubernetes, CI/CD pipelines and infrastructure as code - A track record of taking technical projects from an ambiguous problem statement through to production Read the AI section before you apply Unusually, HackerRank lists AI fluency as its own requirement block rather than a bonus, and asks for: - Deep hands on proficiency with AI powered development tools such as GitHub Copilot, Cursor or Claude Code, to the point where you have built workflows and practices you could teach to others - Strong working knowledge of large language models and agentic systems, including capabilities, limitations, context management and tool use - Proven ability to use AI across the full development lifecycle: architecture exploration, implementation, code review, test generation, documentation, incident analysis and technical writing - Solid understanding of AI and ML fundamentals: transformer architectures, embedding models, inference optimisation, retrieval augmented generation, the tradeoff between fine tuning and prompt engineering, and evaluation methods - The ability to evaluate AI tooling and integration patterns on cost, latency, reliability and security Counts as a bonus, not a requirement - Systems serving millions of concurrent users under strict latency and availability requirements - Deep expertise in patterns such as microservices, CQRS, event sourcing or domain driven design - Significant cloud experience across AWS, GCP or Azure including serverless and cost optimisation - Building platform level APIs, SDKs or developer tools used by other engineering teams - A history of improving engineering culture, through RFC processes, architecture review boards or engineering blog writing Location and working pattern Hybrid in Bangalore, so plan for regular office days. A good fit if You have a few years of backend work and want a product where reliability genuinely matters, and you are already comfortable working with AI tooling rather than avoiding it. Think twice if You have kept AI tools at arm's length. That section is long, specific and clearly being screened on, and it is the part of this posting most likely to decide the outcome.
What the posting requires - 4+ years building and shipping machine learning systems that run in production at scale - Proficiency in Python, with practical experience building data pipelines and deploying models - Systems thinking, which the posting defines precisely: model accuracy, data pipelines, serving infrastructure and customer outcomes are one problem, not four - Treating evaluation methodology as seriously as model performance, on the stated view that a metric measured wrong is worse than no metric Counts as a bonus, not a requirement - Multimodal systems across vision, natural language, audio or behavioural signal pipelines - Large language model experience including fine tuning, RLHF or multi turn agentic systems - A background in adversarial machine learning, fraud detection or anomaly detection - Publications or open source contributions in detection, robustness or evaluation methodology What you would actually be doing - Designing and shipping production machine learning across the Chakra, integrity and evaluation domains - Owning the full lifecycle: problem framing, data strategy, experimentation, deployment and iteration - Building evaluation infrastructure and benchmarking pipelines that measure model quality reliably before and after deployment - Defining the architecture and production bar for new signal categories from scratch - Mentoring junior machine learning engineers and establishing team practice for monitoring, feedback loops and quality standards Location and working pattern Hybrid in Bangalore. The company is explicit about who thrives here The posting says you will do well if messy, undefined problems interest you more than optimising inside clean ones, if ambiguity energises you when the right framing is itself part of the work, and if defining what a system should be appeals more than maintaining what already exists. It also offers direct access to leadership and fast feedback loops. A good fit if You have production machine learning experience and want genuinely unsolved problems. Detecting AI assisted cheating and redefining how coding skill is measured are live, open questions, and few Indian roles work on them directly. Think twice if You want a stable, well specified roadmap, or your machine learning work has been mostly research without deployment. The requirement is specifically shipping systems that run in production at scale.
What the posting requires - Over 8 years building and operating production grade services with clear ownership of outcomes - Strong system design and distributed systems, hands on across AWS or GCP, containers and Kubernetes, infrastructure as code with Terraform, and CI/CD automation - Depth in one or more of Go, Java, Ruby, Python or JavaScript, plus the ability to pick up new languages and frameworks quickly - Proficiency with PostgreSQL or MySQL and NoSQL databases, and experience with event driven architectures using Kafka or similar for streaming and async work - Excellent communication: writing clear documentation, explaining tradeoffs simply, mentoring peers and influencing across teams without direct authority Counts as a bonus, not a requirement - Exposure to AI systems in production such as language model gateways, prompt or model evaluation, and guardrails - Experience improving developer experience across a company - Public artefacts such as design write ups, talks or open source contributions What you would actually be doing - Aligning the engineering roadmap with business outcomes, and communicating the tradeoffs in plain language - Designing and evolving core API, data and AI platforms that stay scalable, observable, secure and cost aware under thousands of concurrent sessions and bursty traffic - Embedding AI as a first class concern: request orchestration, evaluation harnesses, guardrails and safety, and prompt and data governance - Partnering with product managers and customer facing teams to drive impact - Mentoring through reviews, design reviews and hands on pairing Location and working pattern Hybrid in Bangalore. A good fit if You have most of a decade of backend experience and want platform ownership at a product engineers choose to use, and you are as comfortable writing a design document as writing code. The posting says outright that it values balancing long term decisions with fast incremental delivery, and caring about developer experience as much as raw performance. Think twice if You want a pure coding role. A large share of this one is judgement, communication, roadmap and influence, and the posting weights communication unusually heavily for an engineering listing.
What the posting requires - 3 to 5 years as a database developer or engineer, with exposure to migration work - Working experience with at least one of Oracle, Microsoft SQL Server, or PostgreSQL and Aurora PostgreSQL - A bachelor's degree in computer science or a related field What you would actually be doing - Converting SQL, stored procedures and database objects into PostgreSQL compatible SQL and PL/pgSQL, escalating complex or ambiguous cases to a senior engineer - Working through AWS Schema Conversion Tool findings and flagging limitations that need senior review - Maintaining the Flyway migration codebase: versioned scripts, repeatable migrations and rollbacks - Identifying and troubleshooting database related application bugs, with support on the harder ones - Supporting PostgreSQL query optimisation through execution plans, indexing and query rewrites - Monitoring AWS DMS during full load and change data capture phases, and helping diagnose data and performance issues - Validating and reconciling data between source and target databases, which the posting describes as proof rather than confidence - Working with application teams to align SQL behaviour and performance expectations before cutover Location and working pattern Mumbai. This is consulting delivery, so the work is organised around client engagements and committed cutover dates. A good fit if You are a database engineer who wants AWS depth, or a backend engineer who has drifted into data work and wants to make that the specialism. The escalation path is unusually clearly described, which makes it a reasonable step up rather than a role that quietly expects senior judgement. Think twice if You want product engineering. This is client facing modernisation work with the deadlines and delivery pressure that implies, and much of the day is careful, detailed conversion and verification rather than green field building.
What the posting requires Core Python and systems - Strong Python with 4+ years in production: asyncio, performance optimisation and idiomatic code - A solid grasp of distributed systems concepts: state machines, retries, idempotency and eventual consistency - Integrating language model APIs from Anthropic, OpenAI or similar in production code, covering streaming, function calling, structured output, retries and prompt management - Comfort with FastAPI or an equivalent async web framework Cloud and infrastructure - Working knowledge of AWS: EKS, ECS Fargate, S3, DynamoDB, Lambda, Secrets Manager and CloudWatch - Docker and Kubernetes, including writing Dockerfiles, Helm charts and manifests - CI/CD pipelines, with GitHub Actions preferred - Comfort navigating multi account AWS environments across dev, UAT and production Testing and automation - Test driven development as a discipline, with tests written before the code - Hands on pytest, integration testing and end to end testing - The ability to design behaviour verification harnesses: dual run, output comparison and equivalence proof - Using AI tools such as Claude, Copilot or LLM based test generators to improve test quality Explicitly not required The posting states that you do not need prior Amazon Bedrock or LangChain experience. What it wants is a strong Python engineer who will learn agent frameworks properly. That makes this genuinely open to backend engineers who have not worked on agents before, which is unusual for this kind of role. What you would actually be doing - Building and shipping specialised agents: parsers, business rule extractors, dependency mappers, test synthesizers, behaviour replayers and the orchestration between them - Designing agents that operate against a code intelligence graph so every action traces to a line of code - Implementing governed autonomy through shadow, supervised and autonomous stages with human review at each - Orchestrating frontier models from Anthropic, OpenAI and Google, choosing the right model per task - Owning delivery of your agents from prototype through deployment and post release validation The interview process, as published Mactores lists its process openly, which is rare and worth preparing for specifically. There is a hands on technical session with a panel member covering your skills, experience and how you deliver quality under pressure, then a structured panel interview covering experience, product thinking and how you work, then a 30 minute HR discussion about the offer and next steps. The posting also asks applicants to answer as many of the application questions as possible to speed things up. Location and working pattern Mumbai. A good fit if You are a strong Python engineer curious about agent systems but have been put off by postings that demand specific framework experience. Think twice if You dislike heavy test discipline. Testing runs through this entire posting, including test driven development as a stated requirement and the ability to build equivalence proof harnesses.
What the posting requires - A B.E., B.Tech, M.E., M.Tech or PhD in computer science or equivalent - 6+ years with computer science fundamentals and experience with at least one of Go, Java or Python - Proficiency using AI coding tools such as Claude Code, Codex or Cursor across the full software development lifecycle, covering design, code generation, testing, monitoring and release. DoorDash lists this as a requirement rather than a bonus - Prior technical experience in big data infrastructure and in cost efficiency and governance, having built meaningful pieces of data infrastructure - Experience improving the efficiency, scalability and stability of data platforms - Experience building or operating backend systems in production Counts as a bonus, not a requirement - Having built data infrastructure on open sourced technologies such as Spark or ClickHouse What you would actually be doing - Working across data infrastructure, reliability, governance, developer tooling and AI systems - Supporting thousands of internal data users including engineers, analysts, data scientists and business stakeholders - Helping establish a founding engineering team in India, with the ownership and direct influence that implies Location and working pattern Pune. A good fit if You have solid backend or data platform experience and want early team influence rather than a defined slot in an existing structure. Note also that the AI tooling requirement is stated plainly, so make sure you can speak to how you actually use these tools day to day rather than in the abstract. Think twice if You want a settled team, established process and a clear roadmap. Founding teams trade that stability for ownership, and the posting is honest that this is one.
What the posting requires - 8+ years of professional experience as a hands on engineer and technical leader, leading multiple projects - 6+ years working specifically in data platform and data engineering or a similar role - Proficiency in Python, Kotlin or Scala - 4+ years with ETL orchestration and workflow management tools such as Airflow - 4+ years with the distributed data ecosystem such as Spark and Presto, and streaming technologies such as Kafka, Flink or Spark Streaming - Expert level database fundamentals, SQL, data reliability practices and distributed computing - Proficiency using AI coding tools such as Claude Code, Codex or Cursor across the full development lifecycle, which DoorDash asks for explicitly - Excellent communication with both technical and non technical teams, and knowledge of reporting tools What you would actually be doing - Acting as technical lead, architecting and scaling data reliability, infrastructure, automation and tooling - Owning critical data systems that support multiple products and teams - Developing, implementing and enforcing best practices for data infrastructure - Improving the reliability and scalability of ingestion, data processing, ETL, reporting tools and data ecosystem services Location and working pattern Hyderabad. A good fit if You have led data platform work before and want ownership of systems the business genuinely depends on, including financial reporting where correctness is not negotiable. Think twice if You are still early in a data engineering career, or your data work has been alongside another specialism rather than the main thing. The stacked requirements, 8 years overall and 6 in data, mean a generalist backend engineer with some pipeline experience is unlikely to clear the bar.
What the posting requires - 3+ years of hands on experience in applied machine learning - A strong background in machine learning, deep learning and natural language processing, with proven experience training and fine tuning large scale models such as language models, transformers and diffusion models - Hands on expertise with parameter efficient fine tuning approaches: LoRA, prefix tuning, adapters and quantization aware training - Proficiency in PyTorch, TensorFlow and the Hugging Face ecosystem - Basic understanding of MLOps: experiment tracking, model versioning, CI/CD for ML pipelines and production deployment - Experience with large datasets, feature engineering and data pipelines using tools such as Spark, Databricks or cloud native ML services including SageMaker, Vertex AI or Azure - Knowledge of GPU and TPU optimisation, mixed precision training, and scaling workloads on cloud or HPC environments - Excellent communication, able to explain complex ML concepts to technical and non technical audiences Marked mandatory in the posting - Demonstrated success adapting foundation models to domain specific applications through fine tuning or transfer learning - Strong ability to design, evaluate and improve models using robust validation strategies, bias and fairness checks, and performance optimisation Counts as a bonus, not a requirement - Distributed training frameworks such as DeepSpeed, PyTorch Lightning or Ray - Applied AI work across natural language, computer vision or multimodal systems - Proven ability to lead and mentor junior applied scientists and ML engineers Location and working pattern Bengaluru. A good fit if You have a few years of real modelling work and want problems where the business outcome is measurable, since commerce data gives fast, unambiguous feedback on whether a model is helping. Think twice if Your fine tuning experience is thin. The posting marks foundation model adaptation as mandatory rather than preferred, so it is likely to be a hard filter rather than something you can compensate for elsewhere.
What the posting requires - 3 to 6 years designing and developing complex systems - Strong Java, with exceptional software system design, problem solving and object oriented coding skills - Experience with distributed transaction processing systems or asynchronous messaging technology, which the posting states is required rather than preferred - A good understanding of system performance tradeoffs, load balancing and engineering for high availability - A genuine focus on software quality and end to end ownership of the features you build - A BS or MS in computer science, engineering, mathematics, statistics or a similar field Counts as a bonus, not a requirement - Understanding of enterprise information systems, service oriented architectures and operational data stores Read the level carefully The title says Software Development Engineer II, which at many companies signals an early career or new graduate role. The stated requirement starts at three years, so this is squarely mid level. It is one of the clearest examples in today's edition of why the years matter more than the level in the title, and applying to it as a recent graduate would waste your time. Location and working pattern Bengaluru. A good fit if You are a Java backend engineer with a few years behind you who wants data heavy commerce systems and clear ownership of what you build. Think twice if You are a recent graduate, or your Java experience is mostly CRUD web services. The distributed transaction and async messaging requirement is where this posting will actually be decided.
What the posting requires - 4 to 8 years building scalable, highly critical distributed systems - Strong object oriented design skills, the ability to apply design patterns, and what the posting calls an uncanny ability to design intuitive modules and class level interfaces - Strong technical aptitude and good computer science fundamentals - A B.Tech in computer science or equivalent What you would actually be doing - Working on highly maintainable and efficient components and systems - Excelling as an individual contributor, which the posting names as part of the role rather than a consequence of it - Identifying and resolving performance and scalability issues in an increasingly complex enterprise product - Improving the overall quality of the code structure and the product - Designing features alongside architects - Owning scoping, breakdown and implementation independently - Maintaining documentation for the component you work on The scale behind it More than 300 billion events a month, 2,000 plus customers across 40 plus countries. At that volume the interesting problems are throughput, correctness and failure behaviour rather than feature breadth. Location and working pattern Bangalore. A good fit if You have solid backend experience, want data infrastructure at genuinely high event volumes, and specifically want a senior individual contributor track rather than a step toward managing people. Think twice if You are looking for people management or team lead responsibility. The posting deliberately points the other way, and independence is a stated expectation rather than something you grow into.
What the posting requires - 8+ years of experience in software development, from an enterprise background - Experience with popular open source data processing engines such as Spark, Kafka or Flink, or equivalent - Experience developing with concurrent and non blocking systems - A bachelor's degree in computer science, software engineering or a related field, or equivalent experience The broader stack spans Java, Python, Go and C++. What you would actually be doing Engineering on the core pipeline engine behind a platform handling more than 300 billion events a month for over 2,000 customers across 40 plus countries. The problems at that scale are throughput, backpressure, correctness under load and failure behaviour rather than feature breadth. Location and working pattern Bangalore. A good fit if You have deep backend systems experience and want data infrastructure at scale. If you have specifically worked on concurrency and non blocking systems, this posting is asking for you more directly than most listings do, and that is worth leading with in your application. Think twice if Your experience is mainly request and response web services or CRUD applications. The concurrency requirement is called out separately from the general experience bar, which usually means it is a real filter rather than a preference.
What the posting requires - 6+ years of backend engineering building distributed systems, platform services or large scale backend applications - Strong proficiency in one or more common backend languages such as JavaScript, Python, Java, Go, PHP or C++ - Experience designing APIs, frameworks or platform abstractions used by multiple engineering teams - A strong understanding of reliability, observability, operational excellence and system failure modes - Experience with event driven architectures, messaging systems, queues, workflow engines or orchestration platforms - The ability to translate product requirements into durable, scalable platform capabilities - A collaborative mindset, with experience driving alignment across teams that have different priorities - A preference for simple, reliable solutions over unnecessary complexity - Strong communication and the ownership to move ambiguous projects from idea to production Counts as a bonus, not a requirement - Curiosity about AI and agentic product experiences, and how infrastructure needs to evolve to support them Know this before you apply Postman is direct about its in office model. Staff at its hubs in the San Francisco Bay Area, Boston, Austin, New York, Tokyo and London are in office five days a week. For roles based in Bangalore, employees currently work in the office three days a week and will transition to five days per week by the end of the year. If you are optimising for hybrid or remote flexibility, factor that in now rather than at offer stage. Location and working pattern Bengaluru, on the schedule described above. A good fit if You have genuine distributed systems depth, want to build platform capabilities other engineers depend on, and are happy working in an office. Think twice if Remote or hybrid flexibility matters to you. The direction of travel here is explicitly toward five days a week in office, and the posting states it rather than leaving it to be discovered.
What the posting requires - 10+ years of hands on coding, with familiarity across common programming languages such as C, C++, Java, PHP, Python, JavaScript and NodeJS - 10+ years of engineering experience building end to end backend services - Strong experience with security offerings such as vault stores, token scanning and cryptography - Demonstrated experience leading cross functional projects with meaningful impact on the business - Exceptional problem solving, design and analytical skills - Demonstrated experience designing highly scalable systems - A bachelor's degree in a computer science related field, or equivalent practical experience What the work concentrates on Keeping the platform trustworthy for a user base that stores credentials and secrets alongside their API work. The named security areas, vault storage, token scanning and cryptography, point at secret handling and leak prevention rather than general application security. Know this before you apply The same in office model applies as Postman's other Bengaluru opening: employees based in Bangalore currently work in the office three days a week, moving to five days per week by the end of the year. Location and working pattern Bengaluru, on the schedule above. A good fit if You have a decade of backend engineering and want scope over trust, security and reliability rather than feature delivery, and you have real cryptography or secret management experience to point to. Think twice if You are earlier in your career, or your security exposure is general rather than hands on with vaults, tokens and cryptography. Both the coding and the backend service requirements are stated as ten years, so this is not a stretch application.
What the posting requires - A degree in computer science - 3 to 5 years of experience with C and C++ and object oriented programming - Expertise in algorithms and data structures - A working knowledge of Linux systems - Excellent problem solving skills and strong communication - Experience in the high frequency trading industry is listed among the requirements Also mentioned in the posting - Familiarity with other languages including Java, Scala, Python, bash and Lisp - Familiarity with parallel programming models and parallel algorithms - Experience with big data environments What you would actually be doing - Designing and implementing a high frequency automated trading system that trades on multiple exchanges - Building live reporting and administration tools for that trading system - Performance optimisation and improving overall system latency through algorithm research and current technology Location and working pattern Gurugram. How to prepare Quantitative trading firms interview hard on data structures, algorithms, systems internals and low level performance, and Graviton describes its team in those terms. If you are applying, prepare for that specifically rather than for a general backend interview. Domain knowledge of finance is usually not required, and these firms hire primarily on engineering ability, so a strong systems background without trading experience is still worth an application even though HFT experience appears in the list. A good fit if You write performance sensitive C++, enjoy profiling and optimisation, and want work where microseconds are measured and genuinely matter commercially. Think twice if You prefer product breadth or working across a varied stack. This is deep, narrow, latency focused systems engineering, and most of the value you add comes from going further down rather than wider.
What the posting requires - 2 to 5 years of software development experience - 2+ years of product development experience - A BS, MS or PhD in computer science or a related field - Proficiency in one of C++, Java, Go, Scala or Python - The ability to identify scalable and elegant solutions to complex problems - A strong interest in and ability to learn new technologies Counts as a bonus, not a requirement - Full stack development experience What you would actually be doing - Working on the hypervisor protection layer, the most widely protected workload on Rubrik's platform - Building large scale distributed systems that serve both cloud and on premise protection - Designing, developing, testing, deploying, maintaining and improving the products, drawing on your experience with large software systems - Driving large projects end to end, working independently or partnering closely with other engineers Location and working pattern Bangalore. Why this one is worth a look if you are earlier in your career At 2 to 5 years it is the most accessible role in today's edition, and it asks for only one language from a fairly broad list rather than deep expertise across a stack. Distributed systems and storage is also a specialisation that holds its value for a long time, so it is a useful direction to move in early. The posting explicitly values willingness to learn new technologies, which suggests the team expects to bring people up rather than hire fully formed specialists. One practical note Rubrik's application page sits behind bot protection and can be slow to load or refuse automated traffic. We confirmed this posting is live and open. If the link does not open first time, try again in a normal browser window. Think twice if You want to specialise in application or product feature work. This is systems and infrastructure engineering at the storage and virtualisation layer.
What the posting requires - 9+ years of software engineering with a deep backend and infrastructure focus - Strong Python and/or Go. The posting is pointed here, asking for people who ship production code rather than scripts and configuration - Deep hands on Kubernetes: building and operating clusters, not just deploying onto them - Proven experience designing and operating distributed systems in production - Cloud native fluency across AWS and/or GCP spanning compute, storage, identity and access management, networking and managed services - Experience with infrastructure as code using Terraform or similar, and CI/CD pipelines - Familiarity with applied AI tooling and patterns, including agentic AI tools, AI gateways and agent frameworks, and the ability to build backend services that integrate with them - Strong system design and architectural judgement - Clear communication, partnering across product, security and AI teams Counts as a bonus, not a requirement - Observability stacks such as Prometheus, Grafana, Datadog or OpenTelemetry - Multi cloud or hybrid infrastructure experience across AWS, GCP and on premise Read where this role sits Rubrik places this position in its Information Technology organisation rather than core product engineering. That team centralises infrastructure, technology and data management to create operational efficiency as the company scales, and owns the secure environment the development lifecycle runs in. The AI capability you would build is for internal use. Location and working pattern Bangalore. One practical note Rubrik's application page sits behind bot protection and may refuse automated traffic or load slowly. We confirmed this posting is live. If it does not open first time, retry in a normal browser window. A good fit if You are experienced, genuinely interested in enterprise AI and internal platform engineering, and you want deep infrastructure work with modern AI tooling attached. Think twice if You specifically want to build a product customers buy. Rubrik's hypervisor opening in this same edition is the product engineering one, and the two lead to quite different careers.
What the posting requires - 3+ years with Apex, Lightning Web Components, SOQL and JavaScript, working in an Agile team - Force.com coding standards and the Salesforce Lightning Design System - Experience spotting the blind spots and bugs specific to Salesforce - Experience working with and optimising millions of rows of data - CSS, HTML and jQuery - Version control with Git or SVN - Writing unit tests with assertions for core functionality - The ability to turn technical requirements into independent features, with real debugging depth Counts as a bonus, not a requirement - AgentForce, Einstein and other AI tooling - SQL, Python, Data Cloud and Snowflake - Familiarity with a pay as you go business model - Building and deploying through Heroku - Conga or Apttus CPQ or CLM What you would actually be doing - Evolving the Salesforce data model behind Twilio's sales systems so it stays scalable and intuitive for the people using it - Building custom functionality using SOQL APIs, Apex controllers, Apex web services and Apex callouts - Running technical analysis, documenting integration requirements, and working with architects on integration design - Reviewing code quality across the codebase and refactoring for extensibility Location and working pattern Remote, but not from anywhere in India. The posting names five states: Karnataka, Tamil Nadu, Telangana, Maharashtra and New Delhi. Check you are in one before applying. Occasional travel may be required for project or team meetings in person. Also worth knowing Twilio lists competitive pay, generous time off, parental and wellness leave, healthcare and a retirement savings programme, noting that offerings vary by location. Twilio also states that it uses AI to make its hiring process more efficient, while every hiring decision is made by a person. A good fit if You are already a Salesforce developer with around three years behind you, and you want that skill inside a large global product company on a remote Indian contract. Think twice if You are a general backend or full stack engineer. Very little transfers here without Salesforce platform experience, and the required list is specific about it. The other two Twilio roles in today's edition are ordinary software engineering.
What the posting requires - 7+ years of applied machine learning with strong Python - A real grounding in machine learning foundations and the building blocks of modern deep learning - A track record of building, shipping and maintaining models in production in an ambiguous, fast paced environment - Experience designing large scale experiments and analysis that inform a product roadmap - Understanding of why frameworks like PyTorch, TensorFlow or Keras work as they do, not just how to call them - MLOps familiarity: testing, retraining and monitoring models once live - The ability to ramp up quickly in unfamiliar business domains - Exposure to modern data storage, messaging and processing tools such as Kafka, Spark, Hadoop, Presto and DynamoDB, including coding against big data components - Experience working in an agile team with shifting priorities - Experience working on AWS Counts as a bonus, not a requirement - Experience with large language models What you would actually be doing - Building and maintaining scalable machine learning in production - Training and validating deep learning and statistical models, weighing use case, complexity, performance and robustness - Developing an end to end understanding of the systems and the reasoning behind them - Partnering with product managers, tech leads and stakeholders to scope what needs building - Working with data platform teams on robust batch and real time pipelines - Mentoring, knowledge sharing, and upholding code review, automated testing and monitoring practice Location and working pattern Remote and based in India. Unlike Twilio's other two openings today, this one states no restriction to particular states. Occasional travel may be required for in person team meetings. Also worth knowing Twilio lists competitive pay, generous time off, parental and wellness leave, healthcare and a retirement savings programme, varying by location. A good fit if You have several years of production machine learning and want unusual scale plus a genuinely remote Indian role. Think twice if Your experience is mostly notebooks, coursework or research without deployment. The posting returns repeatedly to shipping and maintaining models, not building them once.
What the posting requires - 12+ years designing and building highly available, high scale, secure, mission critical cloud SaaS applications in Java, Go or Python - Knowledge of architectural patterns for mature services, especially distributed and multi regional deployments - Strong high level service design: modelling interfaces and internals so they stay modular, composable and extensible - AWS, GCP or another public cloud, with multi region deployment a plus - Building distributed systems at scale on a microservices architecture - Extensive REST API design and implementation - Data architecture experience such as ClickHouse, Lakehouse or Snowflake - SQL and NoSQL databases and caches - Familiarity with streaming systems such as Kafka, Flink or Spark - Container technologies, Docker and Kubernetes - Modern automated testing and CI/CD pipelines, with genuine ownership of develop, test and deploy - Strong track record solving complex production issues What you would actually be doing - Leading architecture and system design for complex distributed systems, and guiding teams toward maintainable solutions - Writing and reviewing high quality code for core services, APIs and platforms - Turning business goals into technical roadmaps and aligning stakeholders on direction - Improving resiliency, latency, throughput and cost efficiency, and establishing SLOs, SLAs and operational practice - Driving standards for coding, testing, CI/CD, observability, security and documentation - Coaching and mentoring engineers at every level, leading design reviews, postmortems and technical deep dives - Identifying security, scalability and compliance risks early and driving mitigation Location and working pattern Remote, but restricted to five Indian states: Karnataka, Tamil Nadu, Telangana, Maharashtra and New Delhi. Occasional travel may be required for in person meetings. A good fit if You are an experienced architect who wants breadth across many teams while staying hands on, working remotely from India. Think twice if You are below roughly a decade of experience. If you are earlier in your career, the requirement list is still worth reading as a map of what deep distributed systems specialisation builds toward.
What the posting requires - 3 to 5 years as a machine learning engineer, with a track record of building and deploying ML systems - Hands on experience creating generative AI systems including large language models and retrieval augmented generation, plus a clear history of operationalising agentic AI in production - Deep understanding of MLOps and LLMOps, specifically the monitoring, observability and debugging problems created by non deterministic agents - A solid computer science foundation in data structures and algorithms - Python expertise with scikit-learn and PyTorch or TensorFlow, plus SQL for feature engineering and model evaluation - Strong communication, able to work independently and in a team Counts as a bonus, not a requirement - Cloud services such as AWS, GCP or Azure, and ML platforms such as Kubeflow or SageMaker - Familiarity with systems programming or distributed systems - Visible contribution to the field such as technical blogs, papers or industry knowledge sharing What you would actually be doing - Independently developing machine learning models and generative AI solutions, with the emphasis on building and deploying agentic architectures - Driving AI agents end to end into production, handling multi step reasoning, tool use, state management and model orchestration in live environments - Designing LLMOps frameworks that give real observability, monitoring, logging and evaluation for agents - Refining and optimising both generative pipelines and existing classical models through feature engineering and hyperparameter tuning - Working with cross functional teams to turn business needs into technical solutions Location and working pattern A hybrid position based in Bangalore, reporting to the Manager of Machine Learning Engineering. A good fit if You have shipped machine learning before and want to move into agent based systems, on a security product where a mistake has real consequences. Security data is adversarial in a way most recommendation and forecasting work is not, because someone is actively trying to defeat your model. Think twice if You have only used language models through an API. The posting asks specifically for agents you have run in production, and repeats the point about non deterministic behaviour, which is where that experience shows.
What the posting requires - 4+ years in automation testing, preferably within an enterprise or cloud environment - A foundational understanding of AI and machine learning, and experience using AI driven approaches in your own domain, which Zscaler asks for across its engineering roles - The Python, Kubernetes and networking combination named in the title, all three of which the role genuinely depends on What you would actually be doing - Designing scalable, reusable and maintainable test automation frameworks using an AI first development approach - Writing and running end to end tests across ZDX components: cloud services, analytics dashboards, APIs and user facing applications - Working with engineering and DevOps to integrate testing pipelines into CI/CD so release cycles stay fast without losing quality - Using analytics tools, logs and test output to find bottlenecks and troubleshoot systematically - Continuously refining test methodology to improve coverage and catch issues earlier Location and working pattern Bangalore, in the Platform and Reliability Engineering department, reporting to the Director of Platform and Reliability Engineering. Zscaler operates a hybrid working model. A good fit if Your background is QA automation or SDET work and you want to go deeper into Kubernetes and networking. That combination is what makes this more valuable long term than generic test automation, and it is a realistic route out of pure manual or scripted QA. Think twice if You want to build product features. This is a testing, tooling and reliability role. Also do not treat the networking requirement as decoration: it is in the title, and ZDX is a network and digital experience product, so expect it in the interview.
What the posting requires - 5+ years as a software engineer or machine learning engineer - Experience applying machine learning to improve key business metrics, optimising models for both accuracy and latency - Experience researching newer techniques and innovating to build ML based solutions - Experience with the algorithms behind data collection and processing infrastructure, model training, and serving platforms - Deep expertise with AWS Counts as a bonus, not a requirement - A proven track record designing, building and shipping end to end applications at scale - Experience identifying and solving problems in data using machine learning systems - Familiarity with multi agent systems, agent architectures, prompt engineering and current AI frameworks What you would actually be doing - Building and maintaining large scale distributed systems covering the whole pipeline: data collection, feature engineering, model training, evaluation, deployment and real time serving - Writing functional specifications, assessing task requirements and scheduling, and staying involved through development, debugging and support - Solving problems such as threat detection and business intelligence in direct collaboration with data scientists, product management and product engineering Location and working pattern Based in Bangalore, reporting to the Manager of Machine Learning Engineering. Zscaler operates a hybrid working model. A good fit if You have several years of production machine learning and want an adversarial domain. Threat detection behaves very differently from the recommendation and forecasting problems most machine learning engineers see, because the data changes in response to your model. Think twice if You prefer modelling to infrastructure. A large part of this role is the distributed systems underneath the models, and the posting leads with that rather than with modelling technique.
What the posting requires - 13+ years in software development, with at least 8 of those specialising in production grade microservices - Hands on experience in a modern language such as Java, Go or Rust - Expertise in distributed data patterns including CQRS and event sourcing, and event driven communication using Kafka, RabbitMQ or SQS - Extensive hands on Docker and Kubernetes within AWS, Azure or GCP - Proficiency in both SQL (PostgreSQL, MySQL) and NoSQL (Cassandra, MongoDB or Cosmos DB) design and scaling - A foundational understanding of AI and machine learning, and experience applying it in your own domain Counts as a bonus, not a requirement - Implementing AI assisted engineering tools, or integrating predictive models to improve control plane resiliency and automated scaling - Service mesh technologies such as Istio or Linkerd - Practical chaos engineering and advanced performance testing What you would actually be doing - Leading the technical roadmap to modernise enterprise platforms using API first design and microservices - Designing and deploying highly resilient, low latency, event driven solutions, owning them from the API tier through to back end services - Setting coding standards, performing code reviews, and championing test driven and behaviour driven development - Partnering with product, DevOps and system architects to turn business needs into scalable technical solutions - Managing service lifecycles through Docker and Kubernetes, and monitoring health with tools such as Prometheus and Grafana Location and working pattern A hybrid role based in Bangalore, reporting into senior engineering leadership. A good fit if You have well over a decade of backend depth and want architectural ownership across a platform while staying hands on with code. Think twice if You are early or mid career. This is genuinely a 13 year role. If you are starting out, read the requirements as a picture of what deep backend specialisation looks like across a full career rather than as something to apply for now.
What the posting requires - 3 to 6 years in frontend engineering with shipped, production grade products - Deep React proficiency, TypeScript, modern CSS such as Tailwind, and strong component architecture instincts - State management with Zustand, Redux Toolkit or equivalent, plus solid async data patterns - Data visualisation experience with at least one of D3.js, Mapbox or Leaflet, Deck.gl for geospatial, or Recharts, used in production - REST and WebSocket integration, with enough backend awareness to debug across the boundary - Performance engineering: bundle optimisation, lazy loading and rendering performance in low resource environments Counts as a bonus, not a requirement - WebGL or Three.js for 3D visualisation - Electron or desktop application experience - Offline first or progressive web app architecture - Prior work on dashboards, command and control interfaces, or complex enterprise tooling What you would actually be doing - Building production grade interfaces for strategic sector and enterprise AI use cases, including geospatial overlays, simulation interfaces, ontology viewers and data ingestion dashboards - Owning the frontend layer of atoms end to end, from component architecture through deployment into client embedded environments - Building for genuinely constrained conditions: offline first, low bandwidth and limited hardware - Contributing to shared frontend infrastructure, component libraries, the design system and state management patterns - Joining field testing and client feedback loops alongside the deployment engineers Worth knowing before you self reject Sarvam adds a note to this posting saying it is looking for people who can own the outcomes described, not people who match every line of the specification. If the problem interests you and you believe you can do the work, the company is explicitly inviting you to apply. A good fit if You are a frontend engineer who wants to work close to real AI systems rather than on marketing pages, and you find hard constraints interesting rather than irritating. Think twice if You are used to shipping to modern browsers on fast connections and would rather not think about offline behaviour or locked down client machines. That constraint runs through the entire role.
What the posting requires - 3 to 5 years in machine learning engineering or MLOps, with at least one production language model or ML system in continuous operation - Deep model serving expertise using vLLM, TGI, Triton Inference Server or equivalent, including quantised formats such as GGUF, AWQ or GPTQ - Experience fine tuning and adapting models in constrained, on premise or air gapped environments, managing the data pipeline and compute limits those bring - Containerisation with Docker and Kubernetes, or lightweight alternatives such as K3s or K0s for constrained and edge deployments, across varied hardware - Monitoring and observability with Prometheus, Grafana or equivalent, including building custom evaluation dashboards - Python fluency, with familiarity with fine tuning workflows and model evaluation frameworks - CI/CD tooling for ML pipelines such as GitHub Actions, ArgoCD or DVC The signals they say they look for - You have kept a production ML system running under load, and debugged it when it broke - You do not wait for failures, you build systems that warn you before they happen - You write documentation that people other than you actually use What you would actually be doing - Designing and operating model serving infrastructure across on premise and cloud deployments - Building CI/CD pipelines for model updates, rollbacks and evaluation gated releases - Monitoring latency, accuracy drift, throughput and failure modes, and building systems that surface problems before clients notice - Building evaluation infrastructure: harnesses, A/B testing and model comparison tooling for both lab and field use - Managing containerised serving in constrained, air gapped and edge environments - Writing runbooks and operational playbooks for deployment engineers in the field - Owning incident response for model layer failures across every active deployment Location and working pattern Based in Delhi rather than Bengaluru, which is worth noting because most Indian AI roles cluster in Bangalore. A good fit if You would rather keep models running reliably than train them, and you treat uptime and correctness as equally non negotiable. Think twice if You want research or modelling work. This is squarely infrastructure, reliability and operations, and the air gapped and edge constraints mean the usual managed cloud conveniences often are not available.
What the posting requires - 4 to 8 years in data infrastructure, data engineering, platform engineering or site reliability engineering, ideally somewhere operating at significant data scale - Direct experience managing multi terabyte data stores, having personally built or operated systems holding 10TB or more with sustained high throughput ingestion - Deep working knowledge of at least two of PostgreSQL, MongoDB, Elasticsearch, ClickHouse or comparable systems, including tuning, indexing, partitioning and operational management - Production ingestion pipelines built with Kafka, Spark, Airflow, Flink, dbt or equivalent - Strong Python and/or Go, writing production infrastructure tooling and automation - Solid understanding of storage systems and formats: object storage such as S3 or MinIO, columnar formats such as Parquet and ORC, and how to pick the right layer for a workload - Containerisation and orchestration with Docker and Kubernetes in production - Infrastructure as code and deployment automation with Terraform or similar Counts as a bonus, not a requirement - Vector databases or embedding stores such as Milvus, Weaviate, Qdrant or pgvector - Deploying and operating infrastructure in air gapped, on premise or hybrid environments What you would actually be doing - Designing and operating storage architectures across relational, document, vector and object stores at terabyte scale and across multiple data types - Building ingestion pipelines that reliably handle daily influx across both batch and streaming workloads - Making the decisions on indexing strategy, pipeline orchestration and system performance - Owning reliability and performance of the infrastructure layer for your assigned accounts Location and working pattern Delhi based, working closely with teams deployed at client sites. Worth knowing before you self reject As with its other openings, Sarvam notes it wants people who can own the outcomes described rather than people matching every line of the specification. A good fit if You are a data or platform engineer comfortable working at customer sites and making architecture decisions without a managed cloud safety net. Think twice if You want to stay on internal systems with modern tooling. Air gapped and operationally sensitive environments are the norm here, not the exception.
What the posting requires - 5+ years of backend or data platform engineering, with a meaningful part of it designing and operating analytics systems in production - A track record of building zero to one platforms that other engineering teams depend on - ClickHouse in production at scale: sharding, replication, MergeTree variants, materialized views, projection design, query tuning, and the operational side including backups, upgrades and capacity planning - Go for high throughput backend services, with a strong grasp of concurrency, performance and API design across REST and gRPC - Kafka as a primary streaming backbone: partitioning strategy, consumer groups, exactly once and idempotency patterns, and schema registry - Debezium based change data capture, built and operated from transactional databases into analytical stores - Strong systems instincts, reasoning about throughput, latency, cost and failure modes before writing code Counts as a bonus, not a requirement - Spark and Airflow for batch enrichment, backfills and scheduled aggregation - Multi tenant SaaS platforms with row level security, per tenant quotas and tenant aware access control - Building or integrating BI and dashboard tools such as Superset, Metabase or Cube, especially embedded customer facing analytics - Exposure to billing or metering data feeds where correctness is not negotiable What you would actually be building - A high throughput event ingestion API in Go that accepts arbitrary event payloads, validates them, and lands them in ClickHouse with automatic table creation - Change data capture replication from product databases into the analytics store - A tenant aware query API and no code dashboard layer feeding customer analytics, internal reporting and billing Location and working pattern On site in Bengaluru, working with a small team of senior engineers. The posting says this directly, so treat it as an on site role rather than hybrid. A good fit if You want to be the technical owner of a foundational platform rather than a feature contributor on someone else's system, you like writing design docs as much as code, and you think about cost per query as well as latency. Staff here means senior individual contributor, so the scope is architectural. Think twice if You prefer a defined slice of an existing system, or you need hybrid or remote flexibility.
What the posting requires - 4+ years of experience in software development - Proficiency in at least two of Rust, Go and Python - Good networking fundamentals and systems knowledge, with exposure to open source technologies - Strong data structures and algorithms - Experience with or knowledge of operating systems and current technologies Counts as a bonus, not a requirement - Awareness of cloud technologies, Mesos, KVM or QEMU, and NodeJS or React What you would actually be doing - Gathering product requirements and designing solutions with stakeholders across different domains - Developing and maintaining in house cloud architecture components, including procurement, provisioning, patching, migration and retirement of instances - Owning implemented solutions end to end, from inception through deployment, monitoring and regular upkeep - Identifying infrastructure wide and company wide problems and providing scalable solutions - Measuring and increasing compliance of those solutions, and bringing a DevOps mindset to how they are implemented Location and working pattern Bangalore. Also worth knowing PhonePe lists a substantial benefits package for full time staff: medical, critical illness, accidental and life insurance, an employee assistance programme with an onsite medical centre, maternity and paternity benefits with adoption and day care support, relocation and transfer support, provident fund, gratuity and leave encashment, plus higher education assistance, car lease and a salary advance policy. A good fit if You are a backend or infrastructure engineer who wants to write Rust professionally rather than as a side project. Roles that will pay you to work in Rust in India are genuinely uncommon, and that is the strongest reason to look at this one. Think twice if You want consumer product work. This is internal reliability and cloud tooling. The posting also asks for two languages rather than one, so pure Python engineers with no systems language exposure are a weaker match.
What the posting requires - 5 to 9 years of full time iOS app development, with strong fundamentals and end to end exposure - Strong system design and architecture skills specifically for iOS, not general mobile experience - Hands on experience with modern iOS technology: Swift, UIKit and SwiftUI, MVVM or VIPER architecture, Combine, Core Data and dependency injection frameworks - Experience with unit testing and UI testing across full end to end app development - Strong problem solving, plus what the posting describes as curiosity to tinker and explore new paradigms Counts as a bonus, not a requirement - Experience developing SDKs Location and working pattern Pune, which is unusual for PhonePe engineering roles and worth knowing if you are based in Maharashtra. Also worth knowing PhonePe lists medical, critical illness, accidental and life insurance, an employee assistance programme with an onsite medical centre, maternity and paternity benefits including adoption and day care support, relocation and transfer support, provident fund, gratuity and leave encashment, plus higher education assistance, car lease and a salary advance policy. A good fit if You are an experienced iOS engineer who wants consumer scale rather than enterprise app work, and you are interested in the problems that only appear at very large install bases. Think twice if You are earlier in your mobile career. The band starts at five years of full time iOS work specifically, so treat it as a target to grow toward rather than a stretch application. If you are a cross platform developer working mainly in React Native or Flutter, note that the requirements are consistently native.
Read this before you apply
- No entry level roles today. If you are a recent graduate, filter the board to 0 to 1 years and you will still find this week's Veeva, GitLab and Atomicwork openings, which are all still live.
- Twilio's "Applications Engineer 2" is Salesforce development (Apex, Lightning Web Components, SOQL), not general backend. Do not apply expecting a normal backend role.
- Rubrik's "Senior Software Engineer, Enterprise AI" sits inside Rubrik's internal IT organisation, not core product engineering. The Hypervisor role is the product one.
- Both Postman roles are office based in Bengaluru three days a week now, moving to five days a week by the end of the year, per the posting.
- Twilio's Applications Engineer 2 and Principal Software Engineer are remote but only from five states: Karnataka, Tamil Nadu, Telangana, Maharashtra and Delhi. Only the Staff ML role is open across India. Check before you apply.
- Four roles are Sarvam and four are Zscaler. Separate teams on different work, not one job reposted. Two of the Sarvam roles (ML Ops, Embedded Infrastructure) are Delhi based, not Bengaluru.
We read every posting by hand before it goes on this page. The same write-up goes out to the daily email.