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.
Position: Sr. Software Engineer – AI Engineer
At Bristol Myers Squibb, we are inspired by a single vision – transforming patients’ lives through science. Each of our colleagues contribute to innovations that drive meaningful change.
Scope
The EMES core platform, its components, interfaces, data layer and tools – Material and Inventory, Order Management, Equipment and Instrument Integration, Sample Management, Patient Scheduling System integration, Label Printing, Recipe Authoring and Execution, Electronic Batch Records, and Batch Reporting and Analytics – together with:
AI and GenAI services embedded in manufacturing workflows: assistants, retrieval over controlled documents, anomaly detection and decision support.
Web applications and operator-facing interfaces across shop-floor, quality and supply-chain personas.
Enterprise data models, databases, audit-trail architecture and reporting data flows; cloud migration and adoption of next-generation manufacturing systems.
Key Responsibilities
AI Engineering
Design, build and operate production-grade AI services – not prototypes – including retrieval-augmented generation over SOPs, batch records, deviations and equipment documentation, and agentic workflows that act against MES systems under human oversight.
Own the retrieval architecture (chunking, embedding selection, vector storage such as pgvector or OpenSearch, hybrid search, re-ranking, citation fidelity) and LLM application patterns: structured output and tool calling, MCP servers over enterprise systems, orchestration, cost controls and guardrails.
Establish AI evaluation and observability: golden datasets, offline and online evals, regression gates in CI, hallucination and drift detection, tracing and human-in-the-loop review.
Apply classical ML where it is the better tool (anomaly detection, forecasting, predictive quality) and ensure responsible, compliant AI: data classification, IP and PII protection, model and prompt versioning, reproducibility and validation evidence for GxP-relevant use.
Web Application Development
Deliver full-stack applications: React with TypeScript front ends, component libraries and design systems, and responsive, accessible (WCAG) interfaces built for gloved, time-pressured shop-floor use as well as office users.
Build backend services in Python (FastAPI) and/or Java (Spring Boot) exposing REST and GraphQL APIs with OpenAPI-first contracts, secured with OAuth 2.0/OIDC, JWT, SSO and fine-grained RBAC, and write the data access layer with the same rigour as the API (deliberate query design, sound ORM boundaries, pagination, no N+1 access, idempotent writes).
Implement real-time and AI-assisted experiences – WebSockets/SSE for live batch and equipment status, token streaming with inline citations – alongside validation-rich workflows including e-signature interaction patterns.
Own quality and performance end to end: automated tests across the stack (pytest, Jest/Vitest, Playwright), load testing, and CI/CD (GitHub Actions, Jenkins, ArgoCD) with GitOps delivery of application and database changes together against measured p95 targets.
System and Data Architecture
Lead the design of distributed, multi-tenant systems: bounded contexts and Domain-Driven Design, service decomposition, API and event contracts, and evolution strategy. Own the data model end to end – conceptual, logical and physical design, deliberate normalization, and referential integrity,…