Manager, AI Deployment Engineering — Enterprise (proserve, Media, Telco, Private Equity)

OpenAI OpenAI · AI Frontier · San Francisco, CA · Go To Market

Manager for an AI Deployment Engineering team focused on helping enterprise customers (Professional Services, Media, Telco, Private Equity) realize value from OpenAI's technology. The role involves building and leading a team, guiding them in designing and deploying production-grade AI systems, navigating technical and organizational constraints, and translating field insights into product feedback. Requires technical depth, people leadership, customer judgment, and operational rigor.

What you'd actually do

  1. Build, manage, and develop a high-performing team of AI Deployment Engineers supporting enterprise customers across Professional Services, Media and Entertainment, Telecommunications, and Private Equity.
  2. Own the quality and impact of the team’s work across solution design, implementation, production readiness, adoption, and expansion.
  3. Coach the team through complex decisions involving architecture, model selection, evaluations, reliability, latency, safety, security, governance, and cost.
  4. Establish a clear operating model for prioritizing accounts and engagements based on customer need, strategic value, technical complexity, and the potential for repeatable impact.
  5. Serve as a senior technical escalation point during critical launches, production incidents, complex integrations, and high-stakes customer decisions.

Skills

Required

  • Experience managing customer-facing technical teams (Solutions Architects, Deployment Engineers, Forward Deployed Engineers, Technical Account Managers, or similar)
  • Experience building or leading teams responsible for deploying complex software, data, machine learning, or AI systems in enterprise environments
  • Sufficient technical depth to evaluate architectures, ask incisive questions, challenge assumptions, and coach engineers through difficult implementation decisions
  • Experience taking AI, machine learning, or other technically complex systems from prototype to production
  • Understanding of production system requirements (reliability, observability, security, privacy, data governance, evaluation, operational readiness)
  • Experience leading teams through ambiguity, competing priorities, escalations, and rapidly changing products or markets
  • Ability to translate between technical details, customer needs, product strategy, and business outcomes
  • Experience working with large, complex organizations with multiple business units, stakeholder groups, procurement processes, or governance requirements
  • Strong executive presence and ability to build trust with engineering leaders, business executives, security teams, and other senior stakeholders
  • Experience designing operating models, coverage strategies, prioritization frameworks, or repeatable delivery processes for a growing technical organization
  • Strong cross-functional partnership skills

What the JD emphasized

  • deploy AI securely, reliably, and at scale
  • production-grade AI systems
  • complex technical and organizational constraints
  • technical depth
  • people leadership
  • customer judgment
  • operational rigor
  • production deployments
  • sustained adoption
  • senior technical escalation point
  • production incidents
  • complex integrations
  • high-stakes customer decisions
  • production systems
  • reliability
  • observability
  • security
  • privacy
  • data governance
  • evaluation
  • operational readiness

Other signals

  • customer success
  • deployment
  • production-grade AI systems
  • technical leadership
  • operational rigor