Principal Agentic Engineering Lead - Data Modeling in Bangalore, भारत

Job Description :Agentic Engineering - Lead / PrincipalConsulting, Delivery and EngineeringLevel - Mid to Senior level, Tech Lead or Principal Engineer, 7 to 15 yearsEmployment Type - Full-TimeDepartment - AI and Digital EngineeringAbout the Role :This is not a slide-making or prompt-engineering role. We are looking for someone who has built systems that run in production, not demos, not pilots that died after a sprint, and who can sit across the table from a client CTO and tell them what to do https://jobeax.com/link/Obx0e3Go0YuVk3pI halves, and we mean both. You will architect and deliver AI-native programs end to end. You will also carry a point of view on where this market is going, walk clients through it, and help them stand up their own AI capability covering practice structure, operating model, tooling and https://jobeax.com/link/S5WZ4WVua9kdbO1b will report into and replicate the function of a senior AI delivery leader. That means the depth to design the system, the hands to build it, and the presence to defend it in a room full of https://jobeax.com/link/gAwZTVOsjfx8m3eH 8 to 15 years, we are not looking for someone whose engineering career started with LLMs. We want the years before that: the production systems, the outages, the architecture calls that turned out wrong and what you https://jobeax.com/link/pGHhmHGs772VVsH5 Foundation :- Data modelling across SQL and NoSQL, and a clear view of where each one breaks- Event-driven architecture in production with Kafka, RabbitMQ or equivalent. Ordering, replay, idempotency, dead-letter handling- Distributed systems failure modes: partial failure, retries, backpressure, timeouts, circuit breakers- Caching strategy and invalidation, at a scale where getting it wrong hurt- Observability. You have instrumented a system, not just read someone else's dashboard- At least one cloud deeply. Not three superficially- API and integration patterns against real enterprise surfaces such as ERPs, CRMs and data platforms- CI/CD, containers, release engineering- You have owned something in production. On-call, incidents, the whole thingAI Engineering :Two capabilities that do not come free with an engineering background. These are the ones we probe https://jobeax.com/link/XN6EXvE55P5ffjhO and Error Analysis :- You have built an eval set for a non-deterministic system and used it to make a decision- You can decompose a failure: is this a model problem, a retrieval problem, a data problem or a product problem?- You debug agent behaviour systematically, with traces and evidence, not by gut feelContext Engineering :- Deliberate management of what enters the model on each turn, and why- Memory strategy: what persists across steps, across sessions, and what should not- Tool surface design: what the agent can reach, how it is scoped, what happens when a tool fails- Cost and latency as first-class design constraints, not something discovered in the invoiceConsulting and Client Leadership :Equal weight to the engineering. This is a client-facing role in a services business.- Carry a market point of view on models, tooling and delivery patterns, and translate it into what it means for a specific client, in their language- Run discovery workshops, solution reviews and delivery cadences with client teams- Advise clients on standing up their own AI capability: practice structure, operating model, skills, governance, build versus buy, and adoption across their engineering org- Shape and defend technical proposals, PoC plans and roadmaps. Own the story end to end- Translate business problems into architectures for CXO-level stakeholders without hiding behind jargon- Say no when the answer is no. Tell a client when agents are the wrong tool, and be able to explain whyDelivery and Architecture :- Own end-to-end delivery of AI-native programs, from architecture through production- Design multi-agent orchestration using LangGraph, CrewAI or equivalent. Agent topology, tool routing, memory, state, fallback and recovery paths- Integrate agent systems with enterprise systems of record, not toy datasets- Build RAG where RAG is the right answer, from chunking through retrieval, re-ranking and evaluation- Run agentic coding workflows with Claude Code, Cursor, Codex or equivalent, and lead projects where AI writes significant portions of the codebase while you guide, review and ship it- Work with MCP and shared-context tooling. Design what agents are permitted to reach and under what controls- Bring governance into the design from day one: agent identity, privilege scoping, audit, human-in-the-loop, kill switches- Contribute to reusable frameworks and accelerators the wider practice can useTeam and Practice :- Build AI engineering capability across the delivery organisation through mentoring, standards and review quality- Evaluate new models, frameworks and tooling before the hype catches up, and kill the ones that do not earn their place- Contribute to internal knowledge bases and practice assetsWhat We Are Not Looking For :- Someone who lists LLMs on a resume but has only called the API in a notebook- People who explain everything in terms of frameworks they have never deployed- Consultants who can only narrate what others have built- Engineers who cannot hold a client conversation, and client-facing people who cannot buildHow We Will Evaluate You : - Stage 0, System design with no AI. A distributed systems problem with no LLM in it. Event flow, data model, failure handling, scale. Twenty-five minutes.- Stage 1, Technical screen. Walk us through a system you built. Bring an artifact: an eval set, a trace from a run that failed, a postmortem, a cost-per-task number. Something real, not slides.- Stage 2, Architecture discussion. A business problem, live. Design the solution and defend the trade-offs, including the decision not to use agents.- Stage 3, Client simulation. Present your approach to a non-technical executive audience, then advise them on how to build the capability https://jobeax.com/link/Q50BmYHypYSloKqP a theory round :- Expect to walk through something you have actually built: the architecture decisions, what broke in production, what you would do differently. If you cannot do that with specifics, this role is not the right fit. (ref:hirist.tech)