Principal AI Engineer

Working with Us Challenging. Meaningful. Life-changing. Those aren’t words that are usually associated with a job. But working at Bristol Myers Squibb is anything but usual. Here, uniquely interesting work happens every day, in every department. From optimizing a production line to the latest breakthroughs in cell therapy, this is work that transforms the lives of patients, and the careers of those who do it. You’ll get the chance to grow and thrive through opportunities uncommon in scale and scope, alongside high-achieving teams. Take your career farther than you thought possible. Bristol Myers Squibb recognizes the importance of balance and flexibility in our work environment. We offer a wide variety of competitive benefits, services and programs that provide our employees with the resources to pursue their goals, both at work and in their personal lives. Read more: careers.bms.com/working-with-us .

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.

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