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.

Work arrangement
Hybrid