Lead AI Engineer – Agentic Systems | Pharma Industry (Immediate Joiners only, must have Pharma exp)

Lead AI Engineer – Agentic Systems | Pharma Domain Experience: 6–9 Years Openings: 2 Location: Bengaluru Work Mode: Hybrid – 4/5 Days from Office Joining: Immediate / Serving Notice Period – Able to join within 1 week

About the Role

We are looking for a Lead AI Engineer with strong expertise in Agentic AI, LLM systems, RAG, and production-grade AI engineering, along with hands-on experience in the Pharmaceutical / Life Sciences domain. The role involves designing and building sophisticated agentic systems, multi-agent architectures, retrieval pipelines, intelligent document processing solutions, and AI components that operate within regulated pharma environments. The ideal candidate should have a strong combination of Python engineering, LLM/Agentic AI, cloud deployment, RAG, observability, and pharmaceutical data-domain expertise.

Key Responsibilities

• Design and implement complex components of agentic AI pipelines, including multi-agent graphs, tool orchestration, retrieval modules, and memory systems.
• Build agentic solutions using LangGraph, LangChain, AutoGen, CrewAI, or equivalent frameworks.
• Own end-to-end subsystem designs including agent topology, data flows, API contracts, failure handling, and performance considerations.
• Build and optimize production-grade RAG pipelines, covering document ingestion, chunking, embeddings, hybrid search, retrieval evaluation, and latency optimization.
• Integrate AI systems with pharma data platforms such as IQVIA, Symphony Health, Komodo, Veeva, and similar platforms.
• Develop intelligent document processing pipelines for drug labels, clinical study reports, HEOR dossiers, regulatory submissions, and other pharma content.
• Implement production observability covering tracing, latency, cost, quality, drift, and evaluation metrics using LangSmith, Helicone, or equivalent tools.
• Lead CI/CD practices for AI modules using Docker, Kubernetes, Terraform/CDK, automated testing, staging gates, and rollback procedures.
• Translate requirements from medical affairs, commercial analytics, clinical operations, and other pharma stakeholders into technical AI specifications.
• Ensure solutions follow relevant regulated-environment practices, including 21 CFR Part 11, HIPAA-compatible data handling, GxP traceability, and auditability.
• Contribute to AI solutions for KOL mapping, competitive intelligence, signal detection, RWE, HEOR, and regulatory intelligence.
• Act as a technical reference for AI Engineers through code reviews, architecture discussions, design feedback, and technical mentoring.
• Lead component-level design reviews and identify architecture and implementation risks.
• Participate in client-facing technical discussions and communicate complex technical trade-offs clearly.
• Develop reusable agentic AI patterns, guardrails, reference implementations, and engineering best practices.

Mandatory Requirements

Agentic AI & LLM

• 6+ years of software engineering / ML engineering experience.
• 2+ years of recent hands-on experience building and deploying production LLM or Agentic AI systems.
• Hands-on experience with at least two of:
• LangGraph
• LangChain
• AutoGen
• CrewAI
• Experience debugging and troubleshooting agentic framework behavior/internals.
• Hands-on SDK experience with at least one of:
• Anthropic Claude API
• OpenAI Assistants API
• Vertex AI Agent Builder

Python

• Strong expertise in Python and production-quality software development.
• Experience with type annotations, unit/integration testing, packaging, debugging, and performance profiling.

RAG & Vector Search

• Strong hands-on experience designing and implementing RAG pipelines.
• Experience with:
• Embedding model selection
• Vector databases
• Hybrid retrieval
• Retrieval optimization
• RAG evaluation
• Experience with Pinecone, Weaviate, pgvector, or equivalent.
• Hands-on experience with RAGAS or custom evaluation frameworks.

Cloud & DevOps Experience with at least one major cloud platform:

• AWS
• Azure
• GCP

Plus hands-on experience with:

• Docker
• Kubernetes
• Terraform / CDK
• CI/CD pipelines

AI Observability

• Experience with LangSmith, Helicone, or equivalent AI observability platforms.
• Ability to diagnose production issues related to:
• Latency
• Cost
• Quality
• Model behavior
• Agent traces
• Retrieval performance

Architecture & Production Delivery

• Experience owning subsystem architecture, including:
• Agent topology
• Data flows
• API contracts
• Failure handling
• Tool orchestration
• Retrieval
• Memory systems
• Proven track record of deploying at least 2 agentic AI / ML systems to production.
• Experience establishing and tracking performance benchmarks for production AI systems.

Pharma / Life Sciences Domain – Mandatory Candidates must have relevant Pharma / Life Sciences experience, including exposure to regulated data environments. Working knowledge of areas such as:

• Rx / Claims data
• NPI-level analytics
• Brand performance metrics
• Medical Affairs
• Real-World Evidence (RWE)
• Clinical Operations
• HEOR / Market Access
• Regulatory Intelligence
• GxP environments
• 21 CFR Part 11
• HIPAA-compliant data handling

Preferred Skills

• Model Context Protocol (MCP)
• Veeva Vault
• Medidata
• IQVIA
• Symphony Health
• Komodo
• Neo4j
• Amazon Neptune
• Knowledge Graphs
• RLHF / fine-tuning / model adaptation
• Consulting or services-firm delivery experience

Soft Skills

• Strong architectural and problem-solving skills.
• Ability to write clear and concise technical/component specifications.
• Strong communication skills with both technical and non-technical stakeholders.
• Ability to mentor engineers and provide effective design/code feedback.
• Comfortable participating in client-facing technical discussions.