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Summary:
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.
Key Responsibilities:
Cloud-Native Application and API Engineering:
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.
Agent Runtime, Retrieval, and AWS Platform Patterns:
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.
DevOps, Infrastructure, Observability, and Evaluation:
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.
Collaboration, Enablement, and Technical Leadership:
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.