Lead AI Engineer (Agentic Systems, Pharma domain)

Roles & Responsibilities

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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 - Role is based in Bengaluru - Immediate joiner required — must be able to start within the next week - Role is within pharmaceutical and life sciences industry context

Requirements

Ideal Candidate

• Strong Lead AI Engineer Profile with agentic systems architecture expertise and pharma regulated-environment experience
• Mandatory (Experience 1): 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
• Mandatory (Tech skill 1): Must have hands-on proficiency with at least two agentic frameworks (LangGraph, LangChain, AutoGen, CrewAI), with experience debugging framework internals
• Mandatory (Tech skill 2): Must have direct SDK experience with Anthropic Claude API (tool use, streaming), OpenAI Assistants API, or Vertex AI Agent Builder
• Mandatory (Tech skill 3): Must have Python mastery — production-quality code, type annotations, unit and integration tests, packaging, and performance profiling
• Mandatory (Tech skill 4): Must have RAG pipeline depth — embedding model selection, vector stores (Pinecone, Weaviate, pgvector), hybrid retrieval, RAGAS or custom evaluation harnesses
• Mandatory (Tech skill 5): Must have cloud deployment experience with AWS, Azure, or GCP; Docker, Kubernetes, IaC basics (Terraform or CDK), and CI/CD pipelines
• Mandatory (Tech skill 6): Must have agent observability experience — LangSmith, Helicone, or equivalent — with ability to diagnose latency, cost, and quality issues in production traces
• Mandatory (Tech skill 8) : 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.
• Mandatory (Tech skill 7): Must have track record of shipping 2+ agentic or ML systems to production — not just proof-of-concepts — with documented performance benchmarks
• Mandatory (Domain 1): 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)
• Mandatory (Domain 2): Must have exposure to at least one of: medical affairs analytics, RWE, clinical operations data, HEOR/market access, or regulatory intelligence
• Mandatory (Communication): Must be able to write crisp component specifications and communicate architectural trade-offs to both engineers and non-technical stakeholders
• Mandatory (Availability): Must be an immediate joiner or currently serving notice period, able to start within the next week
• Mandatory (Note 1) : Role is Hybrid, WFH flexibility as well upto 6 days a month
• Mandatory (Note 2): CTC is inclusive of 10% variable
• Preferred (Tech skill 1): MCP (Model Context Protocol); Veeva Vault, Medidata, IQVIA, or Symphony Health integrations; knowledge graphs (Neo4j, Amazon Neptune); RLHF/fine-tuning/model adaptation; prior consulting / services-firm delivery experience

Experience: 6–9 years · Hybrid, 4 days in office · 5 days a week · Notice period less than 15 days