Senior AI Application Engineer
Bristol Myers Squibb EU Policy
An application made for this job — a tailored resume and cover letter that speak straight to the posting.
Benefits offered by this job
Job summary
Bristol Myers Squibb is seeking a Senior AI Application Engineer for its Seattle-area AI Venture Studio delivery team. You will be hands-on, building secure cloud-hosted applications, designing APIs and agent runtimes, and delivering MVPs across six two-week sprints in an agile model.
You will work with LangGraph, FastMCP, OpenSearch, AWS services, and frontier LLMs to enable enterprise-scale AI pipelines, context management, and secure, observable software that aligns with pharma-wide needs.
Qualifications
- 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.
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.
- Execute AI Accelerator cycles of six two-week sprints over a 12-week cycle by developing, testing, and validating cloud and agentic AI product increments.
- Develop MCP-accessible services that allow approved agents to read, write, search, and maintain structured (e.g. markdown/YAML) knowledge assets.
- Build MCP/FastMCP read-write-search APIs, permissioned knowledge stores, version control, audit trails, access controls, and integrations with AWS-native storage and identity patterns.
- 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 analytical questions to be grounded in query history and warehouse context.
- Package reusable deployment patterns, starter kits, and golden paths for AWS Fargate, serverless services, containers, and production-adjacent AI applications.
- Create and maintain CI/CD pipelines, environment configuration, automated tests, infrastructure-as-code, and release processes for cloud AI applications.
- Instrument reliability, latency, cost, usage, tracing, and model/agent behavior using LangSmith or similar tools.
- Embed automated quality gates, security scans, regression tests, structured output validation gates, and guardrail checks into delivery pipelines.
- Build sandboxed agent execution environments with recoverable transformations, provenance, and audit workflows.
- Demonstrate MVP progress through bi-weekly demos and technical updates, tracking platform performance, reliability, cost, security, and business-value signals.
- Continuously improve shared platform patterns based on lessons learned across pods and evolving enterprise standards.
Skills
Education
Tools
Job description
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.
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, but not limited to, 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 allow AI Accelerator pods to move quickly without giving up reliability, observability, security, or enterprise alignment.
The role is deeply tied to the AI Accelerator delivery model: 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 leverage the latest technologies to address pharma-specific unsolved problems across R&D, Commercialization, Manufacturing, and Enabling Functions, where critical context is buried in unstructured knowledge files, multimodal documents and reports, operational records, scientific evidence packages, and other evolving knowledge sources.
BMS is an AWS-first engineering environment for these products, so you will default to AWS-native services and patterns while integrating BMS-preferred AI tools such as LangGraph, FastMCP, OpenSearch, Amazon S3 Vectors, Amazon Neptune, PostgreSQL/RDS, Redis, AWS Fargate, LangSmith, and a variety of approved frontier LLM models and APIs.
This is a role for someone excited to work hands-on with the latest AI tools and frontier technologies, pushing the limits of what technology can do to help BMS discover, develop, and deliver innovative medicines.
- 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 of six two-week sprints over a 12-week cycle by developing, testing, and validating cloud and agentic AI product increments.
- Develop MCP-accessible services that allow approved agents to read, write, search, and maintain structured (e.g. markdown/YAML) knowledge assets.
- Build MCP/FastMCP read-write-search APIs, permissioned knowledge stores, version control, audit trails, access controls, and integrations with AWS-native storage and identity patterns.
- 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.
- Package reusable deployment patterns, starter kits, and golden paths for AWS Fargate, serverless services, containers, and production-adjacent AI applications.
- 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.
- Build 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.
- Continuously refine delivery priorities and technical backlog items based on stakeholder feedback, performance results, sprint reviews, and lessons learned throughout MVP development.
- 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.
- 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.
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.
Brisbane - CA - US: $151,280 - $183,319
Cambridge Crossing: $151,280 - $183,319
Princeton - NJ - US: $137,530 - $166,654
Seattle - WA: $151,280 - $183,319
The starting pay range(s) listed above is for full-time employees (FTE). You may also be eligible for additional discretionary incentive cash and stock opportunities. We determine starting pay thoughtfully – carefully considering the nature of the role, required skills, work location, schedule and the knowledge and experience you bring. Final compensation is guided by pay equity principles and applicable employment laws. Compensation programs are reviewed on an ongoing basis and may be adjusted over time to reflect evolving market factors, and individual, team or Company performance.
Subject to the terms and conditions of the applicable plans then in effect, you may be eligible to participate in our comprehensive benefit plans – including wellbeing support, retirement and financial protection benefits, and insurance offerings (medical, dental, vision, life and disability).
U.S.-based exempt employees are eligible for Flexible Time Off (FTO), which provides paid time off without a set accrual limit, subject to manager approval, along with 11 paid company holidays each year.
Non-exempt employees, RayzeBio employees, and employees located in Puerto Rico receive 160 hours of paid vacation annually for new hires (subject to manager approval), 11 paid company holidays, and 3 optional holidays.
Depending on eligibility, employees may also have access to additional time-off benefits, including paid sick leave, up to two paid volunteer days per year, summer hours flexibility, and leaves of absence for medical, personal, parental, caregiver, bereavement, or military needs. Eligible employees also enjoy an annual Global Shutdown between Christmas Day and New Year's Day.
U.S.-based job seekers can explore full benefit offerings at https://careers.bms.com/benefits
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.
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 adastaffingsupport@bms.com. Visit careers.bms.com/eeo-accessibility to access our complete Equal Employment Opportunity statement.
BMS will consider qualified applicants with arrest and conviction records, pursuant to applicable laws in your area.
For roles based in Los Angeles County only: If you live in or expect to work from Los Angeles County if hired for this position, please visit this page for important additional information: https://careers.bms.com/california-residents/
We will never request payments, financial information, or social security numbers during our application or recruitment process. Learn more about protecting yourself at https://careers.bms.com/fraud-protection.
Any data processed in connection with role applications will be treated in accordance with applicable data privacy policies and regulations.
If this posting is missing required information required by local law or incorrect, contact BMS at TAEnablement@bms.comwith the Job Title and Requisition number. Do not send application-related inquiries to this email. To check your application status, please login to your Candidate Home Account.
R1602672 : Senior AI Application Engineer
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