Lead AI Engineer – Agentic Systems, Pharma Domain

Location: Bengaluru

Experience: 6–9 Years

Working Days: 5 Days a Week

About the Role

We are looking for a highly skilled Lead AI Engineer with strong expertise in Agentic AI, LLM systems, RAG pipelines, and production-grade AI engineering, along with hands-on experience in the Pharma domain.

The ideal candidate should have 6+ years of software or ML engineering experience, including at least 2+ years of recent experience building and deploying production LLM or Agentic AI systems in the Pharma domain.

The candidate will be responsible for designing and implementing sophisticated multi-agent systems, tool orchestration, retrieval and memory architectures, while ensuring scalability, observability, security, and regulatory compliance.

Key Responsibilities

- Design and implement complex Agentic AI pipelines, including multi-agent graphs, tool orchestration, retrieval modules, and memory systems.
- Build agentic AI systems using frameworks such as LangGraph, LangChain, AutoGen, CrewAI, or equivalent technologies.
- Own complete subsystem designs covering agent topology, data flows, API contracts, retrieval, memory, tool orchestration, and failure handling.
- Build and optimize production-grade RAG pipelines, including document ingestion, chunking, embedding selection, hybrid retrieval, evaluation, and latency optimization.
- Integrate AI systems with Pharma data platforms such as IQVIA, Symphony Health, Komodo, and Veeva through REST APIs, event-driven integrations, and batch pipelines.
- Develop intelligent document processing pipelines for drug labels, clinical study reports, HEOR dossiers, regulatory submissions, and other Pharma content.
- Implement AI observability using LangSmith, Helicone, or equivalent platforms to monitor traces, latency, cost, quality, and model behavior.
- Develop evaluation frameworks using RAGAS or custom evaluation harnesses.
- Lead CI/CD practices for AI modules, including Docker/Kubernetes containerization, automated testing, staging gates, and rollback procedures.
- Translate requirements from Medical Affairs, Commercial Analytics, Clinical Operations, and other Pharma functions into technical AI specifications.
- Ensure AI systems follow 21 CFR Part 11 auditability, HIPAA-compatible data handling, and GxP traceability requirements.
- Contribute to KOL mapping, competitive intelligence, signal detection, and domain-specific AI agents.
- Provide technical guidance to AI Engineers through code reviews, architecture guidance, mentoring, and technical problem-solving.




- Lead component-level design reviews and identify architecture risks before production deployment.
- Mentor junior engineers on complex technical challenges and document reusable engineering patterns.
- Participate in client-facing technical discussions and communicate complex technical trade-offs clearly.
- Contribute reusable implementations, guardrails, and engineering patterns to the organization's Agentic AI engineering playbook.

Mandatory RequirementsExperience

- 6+ years of software engineering or ML engineering experience.
- At least 2+ years of recent experience building and shipping production LLM or Agentic AI systems.
- Production experience specifically within the Pharma domain.
- Proven track record of shipping at least 2 Agentic AI or ML systems to production beyond POCs.
- Experience owning complete subsystem designs covering agent topology, data flow, API contracts, retrieval, memory, tool orchestration, and failure handling.

Agentic AI and LLM Expertise

- Hands-on experience with at least 2 agentic AI frameworks, such as:
- LangGraph
- LangChain
- AutoGen
- CrewAI
- Experience debugging and troubleshooting framework-level behavior.
- Direct SDK experience with at least one of:
- Anthropic Claude API
- OpenAI Assistants API
- Vertex AI Agent Builder
- Strong understanding of LLM tool use, function calling, streaming, multi-agent orchestration, memory, and retrieval.

Python and Software Engineering

- Robust proficiency in Python.
- Experience with type annotations, unit and integration testing, Python packaging, performance profiling, and production-quality code.
- Strong understanding of code quality, maintainability, and software engineering best practices.

RAG and Retrieval

- Strong hands-on experience building production RAG pipelines.
- Experience with embedding model selection, vector databases, hybrid retrieval, chunking strategies, retrieval optimization, and RAG evaluation.
- Experience with Pinecone, Weaviate, pgvector, or equivalent vector databases.
- Experience with RAGAS or custom evaluation frameworks.

Cloud and DevOps

- Production experience with at least one major cloud platform: AWS, Azure, or GCP.
- Strong experience with Docker and Kubernetes.
- Working knowledge of Infrastructure as Code using Terraform or CDK.




- Experience implementing and maintaining CI/CD pipelines for production AI/ML systems.

AI Observability Experience with LangSmith, Helicone, or equivalent AI observability platforms.

The candidate should be able to diagnose and optimize:

- Production latency
- Token and model costs
- Retrieval quality
- Agent execution traces
- System reliability
- AI output quality

Pharma Domain Requirements The candidate must have working knowledge of Pharma commercial and/or healthcare data environments. Required domain exposure includes knowledge of Pharma commercial data such as:

- Rx and Claims data
- NPI-level analytics
- Brand performance metrics

Experience operating in regulated environments involving:
- GxP
- 21 CFR Part 11
- HIPAA-compatible data handling

Candidates should have experience in at least one of the following areas:
- Medical Affairs Analytics
- Real-World Evidence (RWE)
- Clinical Operations Data
- HEOR / Market Access
- Regulatory Intelligence

Leadership and Communication
- Ability to write clear and concise technical and component specifications.
- Ability to communicate complex architectural decisions and technical trade-offs to technical and non-technical stakeholders.
- Experience mentoring engineers through code reviews, architecture discussions, and technical problem-solving.
- Ability to independently own production AI components and deliver within defined timelines.

Preferred Skills Experience with any of the following will be an advantage:

- MCP (Model Context Protocol)
- Veeva Vault
- Medidata
- IQVIA
- Symphony Health
- Komodo
- Neo4j
- Amazon Neptune
- Knowledge Graphs
- RLHF
- Fine-tuning and Model Adaptation
- Consulting or Technology Services-Firm Delivery Experience

Availability Immediate joiners or candidates currently serving their notice period are preferred.

Candidates who can start within the next week will be given preference.

Pay: ₹2,200,000.00 - ₹3,200,000.00 per year

Application Question(s)

- 1. Do you have 6+ years of experience in Software Engineering or ML Engineering?

- 1. Do you have at least 2 years of recent hands-on experience building and deploying production LLM or Agentic AI systems?

- 1. Do you have production experience specifically in the Pharma domain?

- 1. Have you worked hands-on with at least 2 Agentic AI frameworks such as LangGraph, LangChain, AutoGen, or CrewAI?

- 1. Are you available to work in a hybrid setup from Bengaluru and able to join within the next week or currently serving your notice period?

Work Location: Hybrid remote in Bengaluru, Karnataka

📌 Lead AI Engineer – Agentic Systems, Pharma Domain (Bengaluru)
🏢 AV Career Hub
📍 Bengaluru