Lead AI Engineer – Agentic Systems, Pharma Domain

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

• Strong 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?
• 2. Do you have at least 2 years of recent hands-on experience building and deploying production LLM or Agentic AI systems?
• 3. Do you have production experience specifically in the Pharma domain?
• 4. Have you worked hands-on with at least 2 Agentic AI frameworks such as LangGraph, LangChain, AutoGen, or CrewAI?
• 5. 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