Lead Engineer - Agentic AI
On-siteBengaluru, Karnataka, India
Job Summary
Architect and ship multi-agent systems that operate autonomously across pharma data pipelines, regulatory intelligence workflows, and cross-functional analytics use cases. Write code, own production deployments, and lead a small team executing the same tasks. Design end-to-end agentic systems combining LLMs, multi-agent orchestration, and enterprise data pipelines, while building observability frameworks to track agent behavior and define guardrails. Integrate with upstream pharma platforms like IQVIA and Veeva, and translate commercial analytics and clinical operations workflows into agentic automation. Lead a team of AI engineers, set technical direction, and partner with client-facing teams to scope engagements and own delivery accountability. Champion AI governance practices aligned with industry standards, including auditability and traceability for 21 CFR Part 11-aware environments.
Required Qualifications
- 8+ years in software or ML engineering
- 3+ years with production LLM or agentic AI systems
- Hands-on proficiency with agentic frameworks: LangGraph, LangChain, AutoGen, CrewAI, or equivalent
- Model Context Protocol (MCP) familiarity
- Direct SDK experience: Anthropic (Agents SDK, tool use, Claude API), OpenAI (Assistants API, function calling), Google (Vertex AI Agent Builder, Gemini API)
- Python fluency
- Ability to build, test, and deploy production code, not just notebooks
- Strong RAG architecture skills: chunking strategies, embedding models, vector stores, knowledge graphs for entity-relationship modeling (drug-indication-HCP-trial), hybrid search, retrieval evaluation
- Cloud-native deployment: AWS, Azure, or GCP
- Containerization (Docker, Kubernetes), CI/CD, infrastructure-as-code
- Observability tooling for AI systems: logging agent traces, eval frameworks, cost management, drift detection
- Working knowledge of pharma commercial data ecosystems: Rx/claims data, NPI-level analytics, market access, brand performance
- Familiarity with regulated data environments: GxP, 21 CFR Part 11, HIPAA-compliant data handling, audit trail requirements
- Exposure to at least two of: medical affairs analytics, real-world evidence, clinical operations data, or HEOR/market access workflows
- Comfort reading and reasoning over scientific and regulatory documents: labels, clinical study reports, AMCP dossiers, payer briefs
- 5+ years leading technical teams or delivery workstreams, including mentoring engineers and managing project scope and timelines
- Track record of shipping production AI solutions with measurable business impact, not just proof-of-concepts
- Comfortable in executive-level conversations: scoping engagements, presenting architecture trade-offs, and aligning on governance expectations
- Strong written communication
- Ability to write a crisp technical spec and a clear client-facing proposal without switching tools
Desired Qualifications
- Model Context Protocol (MCP) strongly preferred
- Experience with Veeva Vault, Medidata, or IQVIA platform integrations
- Knowledge of reinforcement learning from human feedback (RLHF) and fine-tuning workflows
- Familiarity with EU AI Act and emerging FDA guidance on AI/ML in clinical and regulatory contexts
- Prior consulting or services-firm experience: multi-client delivery, proposal development, engagement management
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