{"id":1671233,"url":"https://alion.io/job/datazymes-lead-ai-engineer","title":"Lead AI Engineer","company":{"id":3311,"name":"DataZymes","domain":"datazymes.com","url":"https://alion.io/company/datazymes","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":null,"truth_index":null},"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":["Bengaluru, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":24000,"max_usd":53000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":28},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"AutoGen","optional":false},{"name":"CI/CD","optional":false},{"name":"CrewAI","optional":false},{"name":"Docker","optional":false},{"name":"Helicone","optional":false},{"name":"HIPAA","optional":false},{"name":"Hybrid Search","optional":false},{"name":"Kubernetes","optional":false},{"name":"LangGraph","optional":false},{"name":"LangSmith","optional":false},{"name":"LLM Evaluation","optional":false},{"name":"LLM Guardrails","optional":false},{"name":"Machine Learning","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"Rest API","optional":false},{"name":"LangChain","optional":true}],"status":"live","first_seen_at":"2026-10-02T06:43:28Z","employer_posted_date":null,"last_verified_at":"2026-10-02T06:43:28Z","board_verified":false,"closed_at":null,"days_open":1,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":1},"description":"Responsibilities:\nDesign and implement complex components of agentic pipelines, multi-agent graphs, tool orchestration layers, retrieval modules, and memory systems using LangGraph, AutoGen, CrewAI, or equivalent.\nTake ownership of full sub-system designs: define agent topology, data flows, API contracts, and failure handling for a bounded scope.\nBuild and optimise production RAG pipelines: document ingestion, chunking strategy, embedding selection, hybrid search, retrieval evaluation, and latency tuning.\nIntegrate agentic systems with pharma data platforms (IQVIA, Symphony, Komodo, Veeva) via REST, event-driven hooks, and batch pipeline patterns.\nOwn observability for components: instrument trace logging, cost metrics, drift alerts, and evaluation harnesses using LangSmith, Helicone, or equivalent.\nLead CI/CD for owned modules: containerisation (Docker/Kubernetes), automated test suites, staging gate criteria, and rollback procedures.\nTranslate medical affairs, commercial analytics, and clinical ops requirements into agent component specifications.\nApply 21 CFR Part 11 auditability, HIPAA-compatible data handling, and GxP traceability patterns to every component.\nBuild intelligent document processing pipelines for pharma content: drug labels, clinical study reports, HEOR dossiers, and regulatory submissions.\nContribute to KOL mapping, competitive intelligence, and signal detection agents with domain-aware retrieval and reasoning strategies.\nServe as the day-to-day technical reference for AI Engineers on the pod: code review, design feedback, unblocking implementation issues.\nLead component-level design reviews and surface architecture risks before they reach staging.\nPair with junior engineers on hard problems and document patterns and decisions in the team's shared knowledge base.\nRepresent engineering quality in client-facing technical discussions and translate complex trade-offs into plain language.\nContribute reference implementations and guardrail templates to the firm's internal agentic AI 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