{"id":3755061,"url":"https://alion.io/job/zensar-technologies-aes-de-generative-ai-lead","title":"AES - DE - Generative AI Lead","company":{"id":59576,"name":"Zensar Technologies","domain":"zensar.com","url":"https://alion.io/company/zensar-technologies","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"services","is_intermediary":false,"listed_via":null,"ats_vendor":"Oracle","truth_index":{"grade":"A","score":88,"open_postings":49,"ghost_share":0.143,"stale_share":0.102,"repost_share":0.408,"time_to_fill_p50_days":7,"computed_at":"2026-11-10T06:52:35Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"lead","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Pune, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":8,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"Apache Kafka","optional":false},{"name":"CI/CD","optional":false},{"name":"Claude Code","optional":false},{"name":"Context Engineering","optional":false},{"name":"CrewAI","optional":false},{"name":"Cursor","optional":false},{"name":"Human-in-the-Loop","optional":false},{"name":"LangGraph","optional":false},{"name":"LLM","optional":false},{"name":"Model Context Protocol","optional":false},{"name":"Multi-Agent Systems","optional":false},{"name":"OpenAI Codex","optional":false},{"name":"RabbitMQ","optional":false},{"name":"RAG","optional":false},{"name":"SQL","optional":false},{"name":"LangChain","optional":true}],"status":"live","first_seen_at":"2026-11-04T16:29:26Z","employer_posted_date":"2026-11-04","last_verified_at":"2026-11-10T07:32:59Z","board_verified":true,"closed_at":null,"days_open":5,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":5},"description":"Engineering Foundation\nNon-negotiable. This is what we screen on first, and there is no AI in it.\nData modelling across SQL and NoSQL, and a clear view of where each one breaks\n\nEvent-driven architecture in production with Kafka, RabbitMQ or equivalent. Ordering, replay, idempotency, dead-letter handling\n\nDistributed systems failure modes: partial failure, retries, backpressure, timeouts, circuit breakers\n\nCaching strategy and invalidation, at a scale where getting it wrong hurt\n\nObservability. You have instrumented a system, not just read someone else's dashboard\n\nAt least one cloud deeply. Not three superficially\n\nAPI and integration patterns against real enterprise surfaces such as ERPs, CRMs and data platforms\n\nCI/CD, containers, release engineering\n\nYou have owned something in production. On-call, incidents, the whole thing\n\nAgent systems are distributed systems with a non-deterministic component in the middle. If the fundamentals are not there, the AI layer does not survive contact with production.\nAI Engineering\nTwo capabilities that do not come free with an engineering background. These are the ones we probe hardest.\nEvaluation and Error Analysis\nYou have built an eval set for a non-deterministic system and used it to make a decision\n\nYou can decompose a failure: is this a model problem, a retrieval problem, a data problem or a product problem?\n\nYou debug agent behaviour systematically, with traces and evidence, not by gut feel\n\nContext Engineering\nDeliberate management of what enters the model on each turn, and why\n\nMemory strategy: what persists across steps, across sessions, and what should not\n\nTool surface design: what the agent can reach, how it is scoped, what happens when a tool fails\n\nCost and latency as first-class design constraints, not something discovered in the invoice\n\nConsulting and Client Leadership\nEqual weight to the engineering. This is a client-facing role in a services business.\nCarry 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\n\nRun discovery workshops, solution reviews and delivery cadences with client teams\n\nAdvise clients on standing up their own AI capability: practice structure, operating model, skills, governance, build versus buy, and adoption across their engineering org\n\nShape and defend technical proposals, PoC plans and roadmaps. Own the story end to end\n\nTranslate business problems into architectures for CXO-level stakeholders without hiding behind jargon\n\nSay no when the answer is no. Tell a client when agents are the wrong tool, and be able to explain why\n\nDelivery and Architecture\nThe work itself.\nOwn end-to-end delivery of AI-native programs, from architecture through production\n\nDesign multi-agent orchestration using LangGraph, CrewAI or equivalent. Agent topology, tool routing, memory, state, fallback and recovery paths\n\nIntegrate agent systems with enterprise systems of record, not toy datasets\n\nBuild RAG where RAG is the right answer, from chunking through retrieval, re-ranking and evaluation\n\nRun 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\n\nWork with MCP and shared-context tooling. Design what agents are permitted to reach and under what controls\n\nBring governance into the design from day one: agent identity, privilege scoping, audit, human-in-the-loop, kill switches\n\nContribute to reusable frameworks and accelerators the wider practice can use\n\nTeam and Practice\nBuild AI engineering capability across the delivery organisation through mentoring, standards and review quality\n\nEvaluate new models, frameworks and tooling before the hype catches up, and kill the ones that do not earn their place\n\nContribute to internal knowledge bases and practice assets\n\nWhat We Are Not Looking For\nSomeone who lists LLMs on a resume but has only called the API in a notebook\n\nPeople who explain everything in terms of frameworks they have never deployed\n\nConsultants who can only narrate what others have built\n\nEngineers who cannot hold a client conversation, and client-facing people who cannot build\n\nHow We Will Evaluate You\nStage 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.\nStage 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.\nStage 2, Architecture discussion. A business problem, live. Design the solution and defend the trade-offs, including the decision not to use agents.\nStage 3, Client simulation. Present your approach to a non-technical executive audience, then advise them on how to build the capability internally.\nNot 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.\nEngineering Foundation\nNon-negotiable. This is what we screen on first, and there is no AI in it.\nData modelling across SQL and NoSQL, and a clear view of where each one breaks\n\nEvent-driven architecture in production with Kafka, RabbitMQ or equivalent. Ordering, replay, idempotency, dead-letter handling\n\nDistributed systems failure modes: partial failure, retries, backpressure, timeouts, circuit breakers\n\nCaching strategy and invalidation, at a scale where getting it wrong hurt\n\nObservability. You have instrumented a system, not just read someone else's dashboard\n\nAt least one cloud deeply. Not three superficially\n\nAPI and integration patterns against real enterprise surfaces such as ERPs, CRMs and data platforms\n\nCI/CD, containers, release engineering\n\nYou have owned something in production. On-call, incidents, the whole thing\n\nAgent systems are distributed systems with a non-deterministic component in the middle. If the fundamentals are not there, the AI layer does not survive contact with production.\nAI Engineering\nTwo capabilities that do not come free with an engineering background. These are the ones we probe hardest.\nEvaluation and Error Analysis\nYou have built an eval set for a non-deterministic system and used it to make a decision\n\nYou can decompose a failure: is this a model problem, a retrieval problem, a data problem or a product problem?\n\nYou debug agent behaviour systematically, with traces and evidence, not by gut feel\n\nContext Engineering\nDeliberate management of what enters the model on each turn, and why\n\nMemory strategy: what persists across steps, across sessions, and what should not\n\nTool surface design: what the agent can reach, how it is scoped, what happens when a tool fails\n\nCost and latency as first-class design constraints, not something discovered in the invoice\n\nConsulting and Client Leadership\nEqual weight to the engineering. This is a client-facing role in a services business.\nCarry 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\n\nRun discovery workshops, solution reviews and delivery cadences with client teams\n\nAdvise clients on standing up their own AI capability: practice structure, operating model, skills, governance, build versus buy, and adoption across their engineering org\n\nShape and defend technical proposals, PoC plans and roadmaps. Own the story end to end\n\nTranslate business problems into architectures for CXO-level stakeholders without hiding behind jargon\n\nSay no when the answer is no. Tell a client when agents are the wrong tool, and be able to explain why\n\nDelivery and Architecture\nThe work itself.\nOwn end-to-end delivery of AI-native programs, from architecture through production\n\nDesign multi-agent orchestration using LangGraph, CrewAI or equivalent. Agent topology, tool routing, memory, state, fallback and recovery paths\n\nIntegrate agent systems with enterprise systems of record, not toy datasets\n\nBuild RAG where RAG is the right answer, from chunking through retrieval, re-ranking and evaluation\n\nRun 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\n\nWork with MCP and shared-context tooling. Design what agents are permitted to reach and under what controls\n\nBring governance into the design from day one: agent identity, privilege scoping, audit, human-in-the-loop, kill switches\n\nContribute to reusable frameworks and accelerators the wider practice can use\n\nTeam and Practice\nBuild AI engineering capability across the delivery organization through mentoring, standards and review quality\n\nEvaluate new models, frameworks and tooling before the hype catches up, and kill the ones that do not earn their place\n\nContribute to internal knowledge bases and practice assets\n\n 8-12+ years in enterprise software engineering and architecture\n5+ years in cloud-native architecture and large-scale distributed systems\n2-4+ years in GenAI, Agentic AI, LLM, RAG, and AI engineering programs\nProven experience leading multi-million-dollar transformation programs and customer engagements","description_format":"text","description_chars":9348,"description_truncated":false,"requirements":{"experience_years_min":8,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["Artificial Intelligence","IT Consulting & Digital Transformation","IT Outsourcing & Dedicated Teams"],"lifecycle":[{"event":"open","at":"2026-11-04T16:40:06Z"}],"visa":[],"liveness":{"score":66,"band":"ok","label":"Likely open","p_open":1,"p_active":0.884,"p_room":0.75,"age_days":5,"expected_fill_days":7,"reasons":["conf:5","velocity","win:late","comp:brand"],"computed_at":"2026-11-10T06:52:35Z"},"pay":null,"html_url":"https://alion.io/job/zensar-technologies-aes-de-generative-ai-lead","json_url":"https://alion.io/job/zensar-technologies-aes-de-generative-ai-lead.json","meta":{"generated_at":"2026-11-10T11:33:06Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers","about":"Alion is a live layer of people, companies and AI agents: who they are, whether they are real and active right now, what they do and how to work with them, readable by people and by agents and paid per call.","catalog":"https://alion.io/catalog.json","usage":{"tier":"crawler_verified","counted_by":"address","units_charged":1,"used_today":26819,"day_limit":null,"remaining_today":null,"minute_limit":300,"resets_at":"2026-11-11T00:00:00Z"}},"offers":[{"id":"company.slices","title":"One company in depth, by slice","status":"live","price":{"credits":0.1,"usd":0.01,"plus_per_slice":{"credits":0.05,"usd":0.005}},"unit":"per company, plus each slice with data","note":"the employer in depth","call":{"mcp_tool":"get_company","arguments":{"id":59576},"rest":"https://alion.io/mcp/rest/get_company?id=59576"},"human":"https://alion.io/catalog?offer=company.slices&for=job%2Fzensar-technologies-aes-de-generative-ai-lead"},{"id":"market.stats","title":"A market slice: pay, demand and time to fill","status":"live","price":{"credits":2,"usd":0.2},"unit":"per slice","note":"pay, demand and time to fill for this role and place","call":{"mcp_tool":"market_stats"},"human":"https://alion.io/catalog?offer=market.stats&for=job%2Fzensar-technologies-aes-de-generative-ai-lead"},{"id":"job.search","title":"Open jobs by role, technology, place, pay and visa","status":"live","price":{"credits":0.1,"usd":0.01},"unit":"per posting in a list","note":"similar open postings","call":{"mcp_tool":"search_jobs"},"human":"https://alion.io/catalog?offer=job.search&for=job%2Fzensar-technologies-aes-de-generative-ai-lead"},{"id":"company.verify","title":"Is this company real and active right now","status":"pilot","price":null,"unit":"per company","request":{"url":"https://alion.io/catalog/request","method":"POST","body":"{\"offer\": \"company.verify\", \"for\": \"job/zensar-technologies-aes-de-generative-ai-lead\", \"note\": \"what you need it for\"}"},"human":"https://alion.io/catalog?offer=company.verify&for=job%2Fzensar-technologies-aes-de-generative-ai-lead"}],"agent":{"mcp":"https://alion.io/mcp/jobs","mcp_open":"https://alion.io/mcp/open","next":[{"tool":"get_job","arguments":{"id":3755061}},{"tool":"search_jobs","arguments":{"company":"zensar.com","detail":"compact"}},{"tool":"match_people","arguments":{"job_id":3755061},"note":"people open to offers who fit this vacancy"}],"note":"The same data through MCP tools: https://alion.io/mcp/open needs no sign-in (compact searches, postings, people); https://alion.io/mcp with an Alion account has everything. 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