Lead AI Engineer - Agentic Systems, Pharma
Listing checked September 30, 2026 · pay as published by Talentxo
Overview
As a Lead AI Engineer, you will design and build complex agentic pipelines, including multi-agent graphs, tool orchestration, and memory systems, using frameworks like LangGraph, AutoGen, or CrewAI. You will own full sub-system designs, from agent topology to API contracts, and integrate these systems with pharma data platforms such as IQVIA, Symphony, Komodo, and Veeva. Your work will involve building production RAG pipelines, ensuring observability with tools like LangSmith or Helicone, and applying compliance patterns like 21 CFR Part 11 and HIPAA. You will also lead a pod of AI Engineers, providing technical guidance and code reviews. This role sits at the intersection of orchestration complexity, pharma data constraints, and regulatory requirements.
What You'll Do12
- 1Design and implement complex components of agentic pipelines, including multi-agent graphs, tool orchestration layers, retrieval modules, and memory systems, using LangGraph, AutoGen, CrewAI, or equivalent.
- 2Take ownership of full sub-system designs: define agent topology, data flows, API contracts, and failure handling for a bounded scope.
- 3Build and optimize production RAG pipelines: document ingestion, chunking strategy, embedding selection, hybrid search, retrieval evaluation, and latency tuning.
- 4Integrate agentic systems with pharma data platforms (IQVIA, Symphony, Komodo, Veeva) via REST, event-driven hooks, and batch pipeline patterns.
- 5Own observability for components: instrument trace logging, cost metrics, drift alerts, and evaluation harnesses using LangSmith, Helicone, or equivalent.
- 6Lead CI/CD for owned modules: containerization (Docker/Kubernetes), automated test suites, staging gate criteria, and rollback procedures.
- 7Translate medical affairs, commercial analytics, and clinical ops requirements into agent component specifications.
- 8Apply 21 CFR Part 11 auditability, HIPAA-compatible data handling, and GxP traceability patterns to every component.
- 9Build intelligent document processing pipelines for pharma content: drug labels, clinical study reports, HEOR dossiers, and regulatory submissions.
- 10Contribute to KOL mapping, competitive intelligence, and signal detection agents with domain-aware retrieval and reasoning strategies.
- 11Serve as the day-to-day technical reference for AI Engineers on the pod: code review, design feedback, unblocking implementation issues.
- 12Lead component-level design reviews and surface architecture risks before they reach staging.
Requirements12
- 1Senior engineer with track record of shipping production agentic and LLM systems, past proof-of-concepts, who operates in the space where orchestration complexity, pharma data constraints, and compliance requirements collide.
- 2Hands-on proficiency with at least two agentic frameworks (LangGraph, LangChain, AutoGen, CrewAI) with experience debugging framework internals.
- 3Direct SDK experience with Anthropic Claude API (tool use, streaming), OpenAI Assistants API, or Vertex AI Agent Builder.
- 4Python mastery: production-quality code, type annotations, unit and integration tests, packaging, and performance profiling.
- 5RAG pipeline depth: embedding model selection, vector stores (Pinecone, Weaviate, pgvector), hybrid retrieval, RAGAS or custom evaluation harnesses.
- 6Cloud deployment experience: AWS, Azure, or GCP with Docker, Kubernetes, IaC basics (Terraform or CDK), CI/CD pipelines.
- 7Agent observability expertise: LangSmith, Helicone, or equivalent with experience diagnosing latency, cost, and quality issues in production traces.
- 8Working knowledge of pharma commercial data: Rx/claims, NPI-level analytics, brand performance metrics.
- 9Experience operating in regulated data environments: GxP, 21 CFR Part 11, HIPAA-compliant data handling.
- 10Exposure to at least one of: medical affairs analytics, RWE, clinical operations data, HEOR/market access, or regulatory intelligence.
- 11Experience acting as technical lead or senior reviewer for at least one junior engineer or cross-functional delivery workstream.
- 12Ability to write crisp component specifications and communicate architectural trade-offs to both engineers and non-technical stakeholders.
Who Should Apply
Candidates with 6-9 years of experience who have shipped production agentic and LLM systems, including multi-agent graphs and RAG pipelines, will find this role a strong match. The ideal candidate has hands-on proficiency with at least two agentic frameworks, direct SDK experience with Anthropic Claude API, OpenAI Assistants API, or Vertex AI Agent Builder, and deep knowledge of RAG pipelines and vector stores. Experience in regulated data environments such as GxP, 21 CFR Part 11, and HIPAA is essential, as is familiarity with pharma commercial data like Rx/claims and NPI-level analytics. This role is less suitable for engineers without production experience in agentic systems or those lacking exposure to pharma data and compliance. Candidates who have not led or mentored junior engineers may score lower on fit, as the role involves serving as a technical reference and leading design reviews.
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Required Skills
Application Tip
Highlight your experience with at least two agentic frameworks, such as LangGraph and AutoGen, and provide concrete examples of production systems you have shipped. Emphasize any work with Anthropic Claude API or OpenAI Assistants API, and detail your familiarity with pharma data platforms like IQVIA or Veeva. Demonstrating your ability to apply 21 CFR Part 11 and HIPAA compliance in past projects will strengthen your application.
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