Senior AI Engineer

About the role

As a Senior AI Engineer on the AI Venture Studio team, you will be a hands‑on senior individual contributor who leans into artificial intelligence (AI) to design and build impactful solutions that transform patients’ lives. This role is accountable for the AI‑first engineering of cloud‑native applications, agentic AI products, and the knowledge and context infrastructure that powers them. You will work with the latest AI‑centric tools and technologies to design APIs and MCPs, orchestrate cloud services, deploy agent runtimes, implement workflow pipelines, and develop reusable platform patterns that enable AI Accelerator projects to move fast while maintaining reliability, observability, security, and enterprise architectural alignment.

Key Responsibilities

• Cloud‑Native Application and AI Engineering: Design, build, and deliver backend services using Python/FastAPI, TypeScript/Node, or similar technologies that integrate LLM APIs, AI agents, retrieval systems, workflow engines, and enterprise systems.
• Develop MCP‑accessible services, tools, and skills for governed read/write/search access to structured knowledge assets with versioning, auditability, and secure cloud‑native storage.
• Implement secure application patterns for authentication, authorization, enterprise SSO, service‑account management, secrets management, input/schema validation, and secure service‑to‑service communication.
• Partner with frontend engineers to define clean API contracts, streaming response patterns, error handling, and service‑level behaviors for intuitive AI‑powered user experiences.
• Agent Engineering, Orchestration, and Knowledge Systems: Build and operate agentic applications using LangGraph, Claude Agent SDK, and related frameworks, handling workflow state management, tool use, multi‑agent collaboration, and durable execution.
• Design retrieval, memory, and context architectures with AWS‑native services (vector, graph, relational, cache, object storage) to enable grounded, context‑aware AI applications.
• Create evaluation, testing, and observability frameworks that measure agent quality, reliability, latency, cost, and business outcomes.
• Develop reusable platform accelerators, deployment patterns, and golden paths for containerized, serverless, production AI applications on AWS.
• Platform Engineering, DevOps, and Reliability: Build and maintain CI/CD pipelines, infrastructure‑as‑code, automated testing, evaluation frameworks, and release processes for cloud‑native AI applications.
• Measure and improve reliability, quality, latency, cost, and business outcomes through observability and continuous delivery practices.
• Embed security, quality, and reliability controls into delivery pipelines, including vulnerability scanning, regression suites, guardrails, and structured‑output validation.
• Create isolated development and execution environments that support safe experimentation, reproducibility, auditability, and governed promotion of code and data assets.
• Develop and maintain reusable platform patterns, tooling, and engineering standards that accelerate secure, scalable AI application delivery.
• Technical Leadership, Collaboration, and Delivery: Translate business problems into technical architectures and delivery plans aligned with measurable product outcomes.
• Drive MVPs toward production readiness by validating feasibility, reliability, scalability, security, and business value.
• Rapidly prototype, validate, and iterate on emerging AI capabilities to identify scalable patterns and reduce technical uncertainty.
• 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.

Qualifications

Required 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 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.
• Experience building agentic AI applications using frameworks such as LangGraph, LangChain, PydanticAI, Claude Agent SDK, or similar.
• Experience with containers, CI/CD, GitHub‑based workflows, automated testing, environment configuration, and infrastructure‑as‑code (Terraform, AWS CDK, or CloudFormation).
• Practical experience integrating enterprise LLM services (Anthropic preferred, OpenAI, Gemini, Grok, AWS Bedrock, or similar).
• Understanding of architectural patterns, trade‑offs, and engineering practices required to scale AI applications from prototype to production (reliability, observability, security, performance, cost management).
• Working knowledge of secure and responsible AI application patterns (authentication, authorization, enterprise SSO, secrets management, auditability, model evaluation, guardrails).
• Demonstrated ability to use AI coding agents and AI‑assisted development tools (Claude Code, Codex, Gemini CLI, Cursor, Windsurf, GitHub Copilot).
• Strong communication skills and comfort operating within fast‑moving, cross‑functional agile teams.

Desired Qualifications:

• Experience building MCP servers, MCP tools, FastMCP applications, or governed agent tooling ecosystems.
• Designing retrieval, memory, context, and knowledge architectures using vector stores, relational databases, graph databases, and enterprise knowledge systems.
• Developing multi‑agent systems, workflow orchestration patterns, tool‑calling architectures, durable execution, and long‑running agent workflows.
• Evaluation‑driven development, agent testing, benchmark creation, AI observability, and platforms such as LangSmith or promptfoo.
• Designing semantic layers, natural‑language‑to‑SQL systems, analytics agents, or governed data access patterns.
• Building sandboxed execution environments, provenance tracking, version‑controlled data workflows, and audit‑friendly agent architectures.
• Deploying, operating, and supporting production AI applications with real‑world users, SLOs, monitoring, incident response, and operational support.
• Partnering closely with frontend engineers on chat, copilot, citation/provenance, streaming, and human‑in‑the‑loop experiences.
• Active GitHub contributions, open‑source participation, personal projects, or other demonstrated examples of building with modern AI technologies.
• Experience within life sciences, pharmaceutical, healthcare, scientific, or other regulated industries.

Compensation

• Brisbane, CA – US: $151,280 – $183,319
• Cambridge Crossing: $151,280 – $183,319
• Madison – Giralda – NJ – US: $137,530 – $166,654
• Princeton – NJ – US: $137,530 – $166,654
• Seattle – WA: $151,280 – $183,319

The listed ranges are for full‑time employees (FTE). Additional incentive cash and stock opportunities may be available based on eligibility. Final compensation will be decided based on demonstrated experience.

Benefits

• Health coverage: medical, pharmacy, dental, and vision care.
• Well‑being support: BMS Well‑Being Account, BMS Living Life Better, Employee Assistance Programs (EAP).
• Financial well‑being and protection: 401(k) plan, short‑ and long‑term disability, life insurance, accident insurance, supplemental health insurance, business travel protection, personal liability protection, identity theft benefit, legal support, survivor support.
• Work‑life benefits: flexible paid time off (unlimited with manager approval), 11 paid national holidays (U.S. exempt employees).