Manager, AI Deployment Engineering - Health & Life Sciences

OpenAI OpenAI · AI Frontier · Seattle, WA · Go To Market

Manager for AI Deployment Engineering focused on the Health & Life Sciences sector, responsible for leading a team to implement and scale production deployments of generative AI systems for customers in regulated environments. The role involves strategic planning, team management, establishing operational standards, and ensuring secure, compliant AI adoption.

What you'd actually do

  1. Own the strategy and operating model of the HLS AI Deployment Engineering team, ensuring alignment with company objectives and the evolving needs of our customers.
  2. Hire, mentor, and develop a high-impact team of AI Deployment Engineers focused on HLS production deployments
  3. Establish operating mechanisms, delivery standards, and best practices tailored to regulated environments
  4. Foster a culture of technical excellence, customer empathy, and responsible AI deployment
  5. Drive Successful Enterprise Deployments and oversee end-to-end implementation of generative AI applications in production across healthcare and life sciences organizations

Skills

Required

  • 8+ years of experience in technical delivery, solutions engineering, or deployment roles
  • people management experience
  • led enterprise-scale implementations of AI, ML, or platform technologies
  • experience in healthcare or life sciences environments
  • familiarity with clinical research, drug development, regulatory operations, or health system infrastructure
  • understanding of compliance frameworks such as HIPAA, GxP, and global regulatory considerations
  • engaging with executive stakeholders
  • technical depth
  • operating in ambiguous, fast-moving environments
  • building structure where it does not yet exist
  • responsible AI

What the JD emphasized

  • regulated environments
  • HIPAA
  • GxP
  • FDA
  • EMA
  • people management experience
  • healthcare or life sciences environments
  • compliance frameworks

Other signals

  • deployment of generative AI systems
  • integrate AI into critical workflows
  • adoption of AI responsibly in regulated environments
  • move from experimentation to production with OpenAI technologies
  • end-to-end implementation of generative AI applications in production