Principal AI Engineer

Bristol Myers Squibb EU Policy; Hyderabad; On-site; INR 4,200,000 - 6,600,000; Full time

Job summary

Bristol Myers Squibb EU Policy in Hyderabad seeks an AI Engineer to enable enterprise-grade GenAI capabilities using LLM platforms, cloud AI services, and secure API integrations.

You will collaborate with senior engineers, data scientists, and stakeholders to deliver scalable, compliant GenAI solutions that boost productivity and automate processes while aligning with Responsible AI standards.

Qualifications

• Experience designing autonomous multi-agent workflows.
• Strong background in async Python and API architectures.
• Proven track record with LLM platform integration in regulated environments.
• Familiarity with observability and security in enterprise AI.

Responsibilities

• Design, build, and deploy autonomous multi-agent workflows with orchestration frameworks.
• Architect graph-based agent workflows with 10+ nodes across domains.
• Develop reusable agent libraries, platform patterns, and testing frameworks.
• Build production-grade FastAPI services with PostgreSQL and Redis.
• Implement real-time streaming via SSE and WebSocket with RESTful APIs.
• Integrate cloud LLM providers and manage prompts with versioning.
• Implement state persistence and tool-calling protocols for external data sources.
• Develop hybrid intelligence patterns combining LLMs with rule-based logic.
• Ensure security and regulatory compliance across AI solutions.
• Collaborate with data engineers, analysts, and UX; mentor juniors.

Skills

Async Python

LLM integration

Agent orchestration

GenAI patterns

Communication

Education

Bachelor's or Master's in CS/AI/Data Science

Tools

LangGraph

LangChain

CrewAI

Autogen

FastAPI

PostgreSQL

Redis

AWS Bedrock

Azure OpenAI

OpenAI GPT-4

Docker

Git

Job description

Position Summary

We are seeking an AI Engineer to support the AI Enablement Team in developing and deploying enterprise-grade generative AI solutions. This role leverages large language model (LLM) platforms, cloud-based AI services, and modern API integration frameworks to expand the organization's AI capabilities and drive adoption across business functions.

The ideal candidate will have expertise in generative AI application development, prompt engineering, and AI tool integration within regulated enterprise environments. The position is responsible for delivering secure, scalable, and compliant GenAI-powered solutions that enhance employee productivity, streamline information access, and support intelligent automation — in close partnership with senior engineers, data scientists, and cross-functional business stakeholders.

Key Responsibilities

• Design, build, and deploy autonomous multi-agent workflows using orchestration frameworks such as LangGraph, CrewAI, Autogen, or similar, including complex state machines with conditional routing, parallel execution, and error recovery patterns.


• Architect graph-based agent workflows with 10+ nodes involving agent collaboration, task decomposition, and sequential/parallel execution across multiple business domains.


• Develop and maintain reusable agent node libraries, extensible platform patterns, versioning strategies, and testing frameworks (unit, integration, and end-to-end) for agent workflows.


• Build production-grade FastAPI applications with async I/O patterns, integrating PostgreSQL, Redis, and external enterprise services.


• Implement real-time agent streaming using Server-Sent Events (SSE) and WebSocket protocols, alongside RESTful and event-driven API architectures for agent orchestration.


• Integrate cloud-based LLM providers (AWS Bedrock, Azure OpenAI, Anthropic Claude, OpenAI GPT-4) and design prompt management systems with versioning, templating, and dynamic compilation.


• Implement conversation state persistence using Redis checkpointing and build tool-calling protocols (Model Context Protocol, function calling) for external data sources and APIs.


• Develop hybrid intelligence patterns combining LLM reasoning with rule-based logic and statistical analysis, and build response transformation pipelines for structured analytical outputs.


• Integrate observability platforms (Langfuse, LangSmith, or similar) to enable end-to-end agent tracing, telemetry, performance monitoring, and cost optimization across production workflows.


• Implement evaluation frameworks measuring agent success rates, reasoning quality, and output accuracy, while continuously optimizing token usage and LLM costs.


• Ensure enterprise security integration (LDAP, SSO, access control), robust error handling, and compliance with data governance and Responsible AI standards.


• Partner with data engineers, business analysts, and UX teams to translate requirements into scalable agent workflows and streaming interfaces.


• Mentor junior engineers on async Python patterns, agent design, and LLMOps best practices; participate in architecture reviews and contribute to documentation and knowledge sharing.

Qualifications & Experience

• Bachelor's or Master's degree in Computer Science, AI/ML, Engineering, Data Science, or a related discipline.


• 9+ years of software engineering experience, with 2+ years building and deploying production LLM-powered applications.


• Proven experience with:
  
  
  
  • Agentic orchestration frameworks: LangGraph (preferred), LangChain, CrewAI, Autogen, or similar.
  
  
  • Cloud LLM providers: AWS Bedrock, Azure OpenAI, Anthropic Claude, or OpenAI GPT-4.
  
  
  • Production async web frameworks, particularly FastAPI.
  
  
  • Docker containerization, Git version control, and CI/CD pipelines.
  
  
  • Cloud platforms: AWS, Azure, or Google Cloud.
  
  
  
  


• Expert-level async Python programming (asyncio, async/await patterns).


• Demonstrated experience building autonomous agent systems performing multi-step tasks beyond simple chatbots, including conditional logic, state management, and tool-calling patterns.


• Strong understanding of agentic design patterns including ReAct, Plan-and-Execute, and tool-use agents.


• Experience implementing Model Context Protocol (MCP) and agent-to-agent (A2A) communication frameworks.


• Familiarity with LLMOps practices, observability tooling (Langfuse, LangSmith), and prompt engineering at scale (templating, versioning, optimization).


• Excellent analytical, problem-solving, and communication skills with the ability to work effectively in globally distributed teams.


• Prior experience in global life sciences, especially in the GPS functional area, is a plus.


• Experience managing or collaborating with offshore technical development teams and diverse international stakeholders is a plus.