Senior AI Engineer

Position Summary

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 to 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 have access to the latest AI‑centric tools and technologies to support activities such as the design of APIs and MCPs, cloud services orchestration, agent runtime deployment, workflow pipeline implementation, and reusable platform pattern development that enables AI Accelerator projects to move fast without compromising reliability, observability, security, or enterprise architectural alignment.

This role lives inside the AI Accelerator delivery model: six fully agile two‑week sprints across a twelve‑week cycle to build, test, validate, and prepare minimum viable products (MVPs) for broader organizational adoption and scaling. AI Accelerator projects focus on the most challenging and highest‑upside pharma‑specific problems across R&D, Commercialization, Manufacturing, and Enabling Functions where critical context is buried in unstructured knowledge files, multimodal documents and reports, operational records, and scientific evidence packages.

• Ship in cycles by demonstrating engineering progress and lessons learned every two weeks.
• Focus on AI‑first solutioning that prioritizes BMS technology investments (AWS, Claude, Lang Smith, etc.) with the best chance of meeting use case and project success metrics.
• Collaborate effectively with other engineers (AI, data, UI/UX, machine learning) and broader agile AI accelerator product teams.
• Demonstrate a curious and inquisitive mindset with broad technical adaptability while staying hands‑on with frontier AI technologies, AI coding agents, and the latest agentic engineering capabilities.

• Design, build, and deliver backend services and application components using Python/FastAPI, Type Script/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‑accessible services, tools, and skills that enable governed read, write, and search access to structured knowledge assets (e.g., Markdown, YAML), with versioning, auditability, and integration into cloud‑native storage and identity patterns.
• Implement secure application patterns for authentication and authorization, including enterprise SSO, service‑account and machine credential management, secrets management, input/schema validation, and secure service‑to‑service communication.
• Partner with frontend engineers throughout the software delivery lifecycle to define clean API contracts, streaming response patterns, error handling, and service‑level behaviors that enable intuitive AI‑powered user experiences.

• Build and operate agentic applications using Lang Graph, Claude Agent SDK, and related frameworks, including workflow state management, orchestration, tool use, loops, multi‑agent collaboration, and durable execution patterns.
• Develop MCP servers, tools, and skills that expose governed enterprise capabilities to agents through secure, reusable, and observable interfaces.
• Design retrieval, memory, and context architectures using AWS‑native services and data stores, including vector, graph, relational, cache, and object storage patterns that enable grounded and context‑aware AI applications.
• Build evaluation, testing, and observability frameworks that measure agent quality, reliability, latency, cost, and business outcomes while enabling rapid iteration.
• Create reusable platform accelerators, deployment patterns, and golden paths for containerized, serverless, and production AI applications running on AWS.

• Build and maintain CI/CD pipelines, infrastructure‑as‑code, automated testing, evaluation…