Lead AI Engineer [Agentic Systems, Pharma Domain] - Bengaluru Hybrid - 2 Positions - Immediate Joiner

Title: Lead AI Engineer [Agentic Systems, Pharma Domain] - Bengaluru Hybrid - 2 Positions - Immediate Joiner

Location: Bengaluru - Hybrid 4-5 Days - Only Locals - No Relocation

Experience: 6-9 Years | Openings: 2 | Availability: Must start within next week

Key Skills: Python, AWS, Azure, GCP, Docker, Kubernetes, Terraform, CI/CD, Neo4j, Amazon Neptune, LangGraph, AutoGen, CrewAI, LangChain, Anthropic Claude API, OpenAI Assistants API, Vertex AI Agent Builder, Pinecone, Weaviate, pgvector, RAGAS, LangSmith, Helicone, CDK, IQVIA, Symphony, Komodo, Veeva, MCP, Medidata

Roles & Responsibilities:

• Design and implement complex agentic pipelines — multi-agent graphs, tool orchestration, retrieval, memory systems using LangGraph, AutoGen, CrewAI.
• Own full sub-system designs: agent topology, data flows, API contracts, failure handling.
• Build and optimise production RAG pipelines: ingestion, chunking, embeddings, hybrid search, retrieval evaluation, latency tuning.
• Integrate agentic systems with pharma data platforms [IQVIA] via REST, event-driven, batch patterns.[Symphony][Komodo][Veeva]
• Own observability: trace logging, cost metrics, drift alerts, evaluation harnesses using LangSmith, Helicone.
• Lead CI/CD for owned modules: Docker/Kubernetes, test suites, staging gates, rollback.
• Translate medical affairs, commercial analytics, clinical ops requirements into agent specs.
• Apply 21 CFR Part 11 auditability, HIPAA-compatible handling, GxP traceability to every component.
• Build intelligent document processing pipelines for pharma content: drug labels, CSRs, HEOR dossiers, regulatory submissions.
• Contribute to KOL mapping, competitive intelligence, signal detection agents.
• Serve as day-to-day technical reference for AI Engineers: code review, design feedback.
• Lead component-level design reviews and surface risks.
• Pair with juniors and document patterns in shared knowledge base.
• Represent engineering quality in client-facing discussions.
• Contribute reference implementations and guardrail templates to internal agentic AI playbook.

Ideal Candidate:

6+ years software/ML engg, recent 2+ years building & shipping prod LLM/agentic AI in pharma[Mandatory]

Hands-on 2+ agentic frameworks - LangGraph, LangChain, AutoGen, CrewAI + debugging internals[Mandatory]

SDK exp - Anthropic Claude API / OpenAI Assistants API / Vertex AI Agent Builder[Mandatory]

Python mastery - prod-quality, type annotations, tests, packaging, profiling[Mandatory]

RAG pipeline depth - embeddings, Pinecone/Weaviate/pgvector, hybrid retrieval, RAGAS[Mandatory]

Cloud deployment - AWS/Azure/GCP, Docker, K8s, IaC - Terraform/CDK, CI/CD[Mandatory]

Agent observability - LangSmith/Helicone[Mandatory]

Owned full sub-system designs - topology, flows, contracts, failure handling[Mandatory]

Track record shipping 2+ agentic/ML systems to production with benchmarks[Mandatory]

Pharma commercial data - Rx/claims, NPI analytics + regulated - GxP, 21 CFR 11, HIPAA[Mandatory]

Exposure to medical affairs / RWE / clinical ops / HEOR / regulatory intelligence[Mandatory]

Crisp spec writing & architectural communication[Mandatory]

Immediate joiner[Mandatory]

Preferred: MCP, Veeva Vault/Medidata/IQVIA/Symphony, Neo4j/Neptune, RLHF/fine-tuning, consulting background

Interview Process: Tech Round > Tech Round > Final Discussion

To Apply: https://whatsapp.com/channel/0029Vb8qoU5Fy72HMaSEoT2I