Lead AI Engineer (Agentic Systems, Pharma domain)

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
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

Work arrangement
No