Senior AI Application Engineer

At Bristol Myers Squibb, our purpose‑driven culture inspires us to discover, develop, and deliver innovative medicines. We offer meaningful work, growth opportunities, and a supportive environment that values inclusion, wellbeing, flexibility, and comprehensive benefits.

About the role

As a Senior Application Engineer within Bristol Myers Squibb's AI Venture Studio delivery team, you will be a hands‑on senior individual contributor responsible for building secure cloud‑hosted applications, including agentic AI products and cross‑functional knowledge and context infrastructure. You will design APIs, services, infrastructure patterns, deployment pipelines, semantic‑layer evolution patterns for agent context engineering, and agent runtimes that enable AI Accelerator pods to move quickly without sacrificing reliability, observability, security, or enterprise alignment. The role follows an AI Accelerator delivery model of six two‑week sprints over a 12‑week cycle to build, test, validate, and prepare MVPs for scaling in a fully agile model. You will work in an AWS‑first environment, defaulting to AWS‑native services and integrating BMS‑preferred AI tools such as LangGraph, FastMCP, OpenSearch, Amazon S3 Vectors, Amazon Neptune, PostgreSQL/RDS, Redis, AWS Fargate, LangSmith, and approved frontier LLM models and APIs.

Key Responsibilities

• Cloud‑Native Application and API Engineering: Design, build, and operate backend services, APIs, and application components that power AI Accelerator products; develop Python/FastAPI, TypeScript/Node, or similar services that integrate LLM APIs, retrieval systems, workflow engines, and internal enterprise systems; execute AI Accelerator cycles by developing, testing, and validating cloud and agentic AI product increments; build MCP‑accessible services for agents to read, write, search, and maintain structured knowledge assets; implement secure application patterns for authentication, BMS SSO, secrets management, auditability, input validation, and service boundaries; partner with frontend engineers on API contracts, streaming responses, error handling, and service‑level behaviors.
• Agent Runtime, Retrieval, and AWS Platform Patterns: Build and host agentic workflows using LangGraph with state, multi‑agent orchestration, tool execution, fan‑out/fan‑in, and durable checkpoints; develop MCP tool integrations and FastMCP servers; implement retrieval, memory, and context services using AWS data stores such as S3, Athena, PostgreSQL/RDS, ElastiCache/Redis, OpenSearch, Amazon S3 Vectors, and Amazon Neptune; evolve the semantic layer for SQL and natural‑language‑to‑code agents; package reusable deployment patterns, starter kits, and golden paths for AWS Fargate, serverless services, containers, and production‑adjacent AI applications.
• DevOps, Infrastructure, Observability, and Evaluation: Create and maintain CI/CD pipelines, environment configuration, automated tests, infrastructure‑as‑code, and release processes; instrument reliability, latency, cost, usage, tracing, and model/agent behavior using observability and AI evaluation tools such as LangSmith; embed automated quality gates, security scans, regression tests, structured output validation, and responsible AI guardrails; build sandboxed agent execution environments with branching, provenance, and audit workflows; demonstrate MVP progress through bi‑weekly demos and technical updates; continuously improve shared platform patterns based on lessons learned.
• Collaboration, Enablement, and Technical Leadership: Partner with AI Engineers, Data Engineers, Data Scientists, Frontend Engineers, Pod Leads, architects, and product teams; refine delivery priorities and technical backlog; help transition MVPs to production readiness; provide technical coaching via design, code, and architecture reviews; communicate cloud trade‑offs clearly.

Qualifications

• Bachelor’s or higher degree in Computer Science,