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.

- Contribute to KOL mapping, competitive intelligence, and signal detection agents with domain-aware retrieval and reasoning strategies.

- Serve as the day-to-day technical reference for AI Engineers on the pod : code review, design feedback, unblocking implementation issues.

- Lead component-level design reviews and surface architecture risks before they reach staging.

- Pair with junior engineers on hard problems and document patterns and decisions in the team's shared knowledge base.

- Represent engineering quality in client-facing technical discussions and translate complex trade-offs into plain language.

- Contribute reference implementations and guardrail templates to the firm's internal agentic AI playbook.

Ideal Candidate :

Profile :

- Strong Lead AI Engineer Profile with agentic systems architecture expertise and pharma regulated-environment experience.

Experience :

- 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.

Tech Stack :

- Frameworks : LangGraph, LangChain, AutoGen, CrewAI.

- LLM APIs : Anthropic Claude, OpenAI Assistants API, Vertex AI Agent Builder.

- Python : Production-quality code, type annotations, unit and integration tests, packaging, and performance profiling.

- RAG : Embedding model selection, vector stores (Pinecone, Weaviate, pgvector), hybrid retrieval, RAGAS.

- Cloud/DevOps : AWS, Azure, or GCP; Docker, Kubernetes, Terraform/CDK, CI/CD pipelines.

- Observability : LangSmith, Helicone.

Domain Expertise :

- Pharma commercial data (Rx/claims, NPI-level analytics, brand performance metrics).

- Regulated environments (GxP, 21 CFR Part 11, HIPAA).

- Medical affairs analytics, RWE, clinical operations, HEOR/market access, or regulatory intelligence.

Preferred Skills :

- MCP (Model Context Protocol); Veeva Vault, Medidata, IQVIA, or Symphony Health integrations; knowledge graphs (Neo4j, Amazon Neptune); RLHF/fine-tuning/model adaptation.

Availability :

- Immediate joiner or currently serving notice period, able to start within the next week.