Position : Lead AI Engineer (Pharma domain)

Total Experience : 6 - 9 years

Location : Bengaluru

Working Days : 5 Days from Office

Notice Period Requirement : Immediate Joiners Only

Roles & Responsibilities :

- Design and implement complex components of agentic pipelines - multi-agent graphs, tool orchestration layers, retrieval modules, and memory systems - using LangGraph, AutoGen, CrewAI, or equivalent.

- Take ownership of full sub-system designs: define agent topology, data flows, API contracts, and failure handling for a bounded scope.

- Build and optimise production RAG pipelines: document ingestion, chunking strategy, embedding selection, hybrid search, retrieval evaluation, and latency tuning.

- Integrate agentic systems with pharma data platforms (IQVIA, Symphony, Komodo, Veeva) via REST, event-driven hooks, and batch pipeline patterns.

- Own observability for components: instrument trace logging, cost metrics, drift alerts, and evaluation harnesses using LangSmith, Helicone, or equivalent.

- Lead CI/CD for owned modules: containerisation (Docker/Kubernetes), automated test suites, staging gate criteria, and rollback procedures.

- Translate medical affairs, commercial analytics, and clinical ops requirements into agent component specifications.

- Apply 21 CFR Part 11 auditability, HIPAA-compatible data handling, and GxP traceability patterns to every component.

- Build intelligent document processing pipelines for pharma content: drug labels, clinical study reports, HEOR dossiers, and regulatory submissions.

Candidate Requirements :

- Must have 6+ years of software or ML engineering, with at least recent 2+ years building and shipping production LLM or agentic AI systems in pharma domain.

- Must have hands-on proficiency with at least two agentic frameworks (LangGraph, LangChain, AutoGen, CrewAI), with experience debugging framework internals.

- Must have direct SDK experience with Anthropic Claude API (tool use, streaming), OpenAI Assistants API, or Vertex AI Agent Builder.

- Must have Python mastery - production-quality code, type annotations, unit and integration tests, packaging, and performance profiling.

- Must have RAG pipeline depth - embedding model selection, vector stores (Pinecone, Weaviate, pgvector), hybrid retrieval, RAGAS or custom evaluation harnesses.

- Must have cloud deployment experience with AWS, Azure, or GCP; Docker, Kubernetes, IaC basics, and CI/CD pipelines.

- Must have agent observability experience - LangSmith, Helicone, or equivalent - with ability to diagnose latency, cost, and quality issues in production traces.

- Must have owned full sub-system designs - agent topology, data flows, API contracts, failure handling - for a bounded scope, and built multi-agent graphs, tool orchestration, retrieval, and memory systems.

- Must have track record of shipping 2+ agentic or ML systems to production - not just proof-of-concepts - with documented performance benchmarks.

- Must have working knowledge of pharma commercial data (Rx/claims, NPI-level analytics, brand performance metrics) and experience operating in regulated data environments (GxP, 21 CFR Part 11, HIPAA-compliant data handling).

- Must have exposure to at least one of: medical affairs analytics, RWE, clinical operations data, HEOR/market access, or regulatory intelligence.

- Must be able to write crisp component specifications and communicate architectural trade-offs to both engineers and non-technical stakeholders.

- Must be an immediate joiner or currently serving notice period, able to start within a week's time.