Senior Engineering Manager, AI Platform

Role overview

Build and lead an applied AI engineering group responsible for production agents and assistants used in operational, clinical, and patient-facing workflows. This is a hands-on technical leadership role: set the architecture and quality bar, guide delivery from prioritization through ongoing operations, and grow the team as its scope expands. Systems handle sensitive patient information, so reliability, privacy, safety, and measurable performance are central to the work.

Responsibilities - Hire, develop, and manage senior and staff-level applied AI engineers, with potential to grow the group into multiple teams. - Set the roadmap and oversee production delivery of LLM-based agents, from scoping and launch through maintenance. - Guide architecture for agent orchestration, retrieval, model access, and observability; make tradeoffs with senior technical partners. - Establish evaluation datasets, regression checks, and other measurable release criteria to detect quality changes. - Own operational readiness, including incident response, on-call, provider fallbacks, model upgrades, and AI cost tracking. - Work with product, operations, clinical, security, and compliance partners to prioritize workflows, protect sensitive data, and determine when human review is needed.

Requirements - Bachelor’s degree in computer science, engineering, or a related technical field. - 10+ years in software engineering, including at least 3 years directly managing engineers through hiring, performance, and career development. - Experience leading or building LLM systems used in production by real users. - Track record managing senior and staff engineers and earning their technical trust. - Strong understanding of agent systems, retrieval, evaluation, observability, and production engineering practices, including APIs, testing, deployment, incidents, and on-call. - Experience leading teams that handle sensitive data securely, plus clear communication across technical and nontechnical groups.

Nice to have - Experience in healthcare, finance, insurance, or another regulated field; familiarity with patient data and HIPAA is relevant. - Experience with Google Cloud, scaling engineering teams, model or provider migrations, machine learning beyond LLMs, or internal platforms.

Benefits and work setup - Benefits listed for salaried roles include medical, dental, and vision coverage; flexible spending or health savings accounts; flexible paid time off; a 401(k) with company match; and life and pet insurance.