Senior Cloud Engineer

About the role

Bristol Myers Squibb is seeking a Senior Cloud Engineer to join the AI Venture Studio team. This role is deeply tied to the AI Accelerator delivery model, which involves six two-week sprints over a 12-week cycle to build, test, validate, and prepare Minimum Viable Products (MVPs) for scaling.

Responsibilities

• 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.
• Develop MCP-accessible services that allow approved agents to read, write, search, and maintain structured knowledge assets.
• Implement secure application patterns for authn/authz, BMS SSO, BMS Cloud Creds, secrets management, auditability, input validation, and safe service boundaries.
• Partner with frontend engineers to define clean API contracts, streaming response patterns, error handling, and service-level behaviors for AI-powered user experiences.
• Build and host agentic workflows using LangGraph, including workflow state, multi-agent orchestration, tool execution, fan-out/fan-in patterns, and durable checkpoints.
• Develop MCP tool integrations and FastMCP servers that allow agents to use governed enterprise capabilities safely and consistently.
• Implement retrieval, memory, and context services using AWS-aligned data stores such as S3, Athena, PostgreSQL/RDS, ElastiCache/Redis, OpenSearch, Amazon S3 Vectors, and Amazon Neptune.
• Build and evolve the semantic layer for SQL and other natural-language-to-code generating agents, enabling novel analytical questions to be grounded in query history, column values, warehouse context, explicit instructions, memory, and governed data tools.
• Create and maintain CI/CD pipelines, environment configuration, automated tests, infrastructure-as-code, and release processes for cloud AI applications.
• Instrument application reliability, latency, cost, usage, tracing, and model/agent behavior using enterprise observability and AI evaluation tools such as LangSmith or similar platforms.
• Embed automated quality gates, security scans, regression tests, structured output validation gates, and responsible AI guardrail checks into delivery pipelines.
• Create sandboxed agent execution environments where code and data can branch together, transformations are recoverable, provenance is preserved, and merge/audit workflows protect shared data assets.
• Demonstrate MVP progress through bi-weekly demos and technical updates, tracking platform performance, reliability, cost, security, and business-value signals to assess readiness for scaling.
• Continuously improve shared platform patterns based on lessons learned across pods, changing enterprise standards, and advances in AI engineering practices.
• Partner with AI Engineers, Data Engineers, Data Scientists, Frontend Engineers, Pod Leads, architects, and product teams to solve complex delivery challenges.
• Help complete MVP transition activities by maturing AI capabilities, adding key features, validating reliability in practice, confirming business value, and assessing production readiness.
• Provide technical coaching through design reviews, code reviews, architecture reviews, incident learning, documentation, and reusable examples.
• Communicate cloud trade-offs clearly, including when to optimize for speed, cost, reliability, compliance, scalability, or long-term maintainability.

Qualifications & Experience

• Bachelor's or higher degree in Computer Science, Engineering, Science, or a related field.
• 5+ years of experience in software engineering, cloud engineering, platform engineering, or backend application development with increasing responsibility.
• Hands-on experience building cloud-native applications on AWS; familiarity with services such as S3, RDS/PostgreSQL, Athena, ElastiCache/Redis, OpenSearch, Fargate, Lambda, IAM, and VPC patterns.
• Strong proficiency in Python, FastAPI, TypeScript/Node, or comparable backend application frameworks.
• Experience with containers, CI/CD, GitHub-based workflows, automated testing, environment configuration, and infrastructure-as-code such as Terraform, AWS CDK, or CloudFormation.
• Experience building LLM, RAG, or agentic AI applications using frameworks such as LangGraph, LangChain, PydanticAI, Claude Agent SDK, or similar tools.
• Familiarity with MCP/FastMCP, read-write-search APIs, permissioned markdown/YAML stores, vector databases, knowledge graphs, session/state management, structured output validation gates, and evaluation-driven development.
• Experience with SQL, semantic layers, data warehouse context, query history, and systems that translate LLM-derived meaning from unstructured scientific or operational sources into governed data/context layers.
• Experience building sandboxed execution, data branching, provenance, version control, audit, and access-control patterns for agentic or data-intensive applications.
• Practical experience integrating with model providers and a variety of approved frontier LLM models through enterprise AI services such as OpenAI, Anthropic, Gemini, AWS Bedrock, or similar approved channels.
• Effective use of coding agents or AI-assisted development tools such as Claude Code, Codex, Gemini CLI, GitHub Copilot, or similar tools.
• Excitement for experimenting with the latest AI tools and technologies while turning frontier prototypes into reliable foundations that help discover, develop, and deliver innovative medicines.
• Curious and inquisitive mindset, with strong communication skills and comfort operating in fast-moving, cross-functional agile teams.