Senior AI Engineer - Agentic AI & Cloud-Native Apps

Bristol Myers Squibb

Seattle (WA)

On-site

USD 151,280 - 183,319

Full time

14 days+

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Benefits offered by this job

Health Coverage

Wellbeing Support

Financial Well-being and Protection

Paid Time Off

Diversity & Inclusion

Job summary

Bristol Myers Squibb in Seattle, WA is seeking a Senior AI Engineer to join the AI Venture Studio. You will be a hands-on, senior contributor designing and building AI-first cloud-native solutions that power patient outcomes and enterprise workflows.

Lead API design, MCPs, agent runtimes, and knowledge architectures, collaborating across AI, data, UI/UX, and platform teams to deliver MVPs every sprint while ensuring reliability and security at scale.

Qualifications

Bachelor’s or higher degree in Computer Science, Engineering, Science, or a related field.
5+ years of experience designing, building, and scaling software applications, cloud platforms, APIs, or distributed systems with increasing technical responsibility.
Strong proficiency in Python and FastAPI and/or TypeScript/Node, or comparable backend application frameworks.
Hands-on experience building and operating cloud-native applications on AWS.
Experience designing and delivering AI-powered applications that integrate LLMs, retrieval systems, workflows, agents, or other AI capabilities into real-world user experiences.
Experience building agentic AI applications using frameworks such as LangGraph, LangChain, PydanticAI, Claude Agent SDK, or similar technologies.
Experience with containers, CI/CD, GitHub-based workflows, automated testing, environment configuration, and infrastructure-as-code such as Terraform, AWS CDK, or CloudFormation.
Practical experience integrating enterprise LLM services such as Anthropic (preferred), OpenAI, Gemini, Grok, AWS Bedrock, or similar enterprise-approved AI services into AI-powered applications.
Demonstrated understanding of the architectural patterns, trade-offs, and engineering practices required to scale AI applications from prototype to production, including reliability, observability, security, performance, and cost management.
Working knowledge of secure and responsible AI application patterns including authentication, authorization, enterprise SSO, secrets management, auditability, model evaluation, guardrails, and secure application architecture.
Demonstrated ability to effectively use AI coding agents and AI-assisted development tools such as Claude Code, Codex, Gemini CLI, Cursor, Windsurf, or GitHub Copilot to accelerate software delivery.
Strong communication skills and comfort operating within fast-moving, cross-functional agile teams where ambiguity, rapid experimentation, and continuous learning are expected.

Responsibilities

Translate business problems into technical architectures and delivery plans that align AI capabilities with measurable product outcomes.
Drive MVPs toward production readiness by validating technical feasibility, reliability, scalability, security, and measurable business value.
Rapidly prototype, validate, and iterate on emerging AI capabilities to identify scalable patterns and reduce technical uncertainty before broader adoption.
Provide technical leadership through architecture reviews, code reviews, design mentorship, engineering standards, documentation, and reusable reference implementations.
Communicate technical and cloud architecture trade-offs clearly, balancing speed, cost, reliability, security, compliance, scalability, and long-term maintainability.
Design, build, and deliver backend services and application components using Python/FastAPI, TypeScript/Node, or similar technologies that integrate LLM APIs, AI agents, retrieval systems, workflow engines, and enterprise systems to create scalable AI-powered solutions.
Develop MCP servers, tools, and skills that expose governed enterprise capabilities to agents through secure, reusable, and observable interfaces.
Build and operate agentic applications using LangGraph, Claude Agent SDK, and related frameworks, including workflow state management, orchestration, tool use, loops, multi-agent collaboration, and durable execution patterns.
Create reusable platform accelerators, deployment patterns, and golden paths for containerized, serverless, and production AI applications running on AWS.
Build evaluation, testing, and observability frameworks that measure agent quality, reliability, latency, cost, and business outcomes while enabling rapid iteration.