At Bristol Myers Squibb, our employees often ask, “Who are you working for?”—a question that fuels collaboration, accountability, and urgency in our work. Our purpose-driven culture inspires us to discover, develop, and deliver innovative medicines to prevail over serious diseases. We offer uniquely interesting and meaningful work, opportunities for growth, and a supportive environment that values inclusion, wellbeing, flexibility, and comprehensive benefits. This is work that transforms the lives of patients, and the careers of those who do it.
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.
We hire for skills and capabilities, not just credentials – if this role excites you, but doesn’t perfectly match your resume, we encourage you to apply anyway.
How We Work
Where you work matters – because collaboration, innovation and patient impact happen in many settings. Our roles are structured across four work models: site-essential, site-by-design, field-based and remote-by-design. The model assigned to this role is based on its core responsibilities. Learn more at https://careers.bms.com/ways-of-working.
Supporting People with Disabilities
BMS is dedicated to ensuring that people with disabilities can excel through a transparent recruitment process, reasonable workplace accommodations/adjustments and ongoing support in their roles. Applicants can request a reasonable workplace accommodation/adjustment prior to accepting a job offer. If you require reasonable accommodations/adjustments in completing this application, or in any part of the recruitment process, direct your inquiries to [email protected]. Visit to access our complete Equal Employment Opportunity statement.
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R1604475 : Principal AI Engineer