AI Deployment Engineer, Enterprise

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

This role focuses on deploying AI systems for enterprise customers, translating business needs into technical solutions, building and evaluating AI systems, and ensuring their production readiness, reliability, and impact. It involves hands-on engineering, customer partnership, and influencing product development.

What you'd actually do

  1. Partner directly with enterprise customers to identify high-value opportunities and translate them into technical architectures, implementation plans, evaluation strategies, and measurable success criteria.
  2. Design, build, and deploy AI systems that solve important customer problems and produce measurable business outcomes.
  3. Work hands-on in code to build prototypes, evaluation harnesses, reference implementations, integrations, and production accelerators.
  4. Make sound technical decisions across models, agents, retrieval, tools, data, reliability, observability, latency, cost, safety, security, and governance.
  5. Diagnose complex implementation challenges, reproduce failures, test hypotheses, and drive blockers toward resolution.

Skills

Required

  • Python
  • designing AI systems
  • building AI systems
  • delivering AI systems
  • enterprise environments
  • prototype to production
  • code contributions
  • architecture contributions
  • evaluation contributions
  • debugging contributions
  • production engineering contributions
  • evaluating AI systems systematically
  • enterprise production requirements
  • integrations
  • reliability
  • observability
  • security
  • privacy
  • data governance
  • performance
  • cost
  • connecting technical decisions to customer workflows, adoption, and measurable business outcomes
  • communication with clarity and credibility
  • high agency
  • strong technical judgment
  • end-to-end ownership
  • ambiguous environments
  • learning quickly
  • challenging assumptions constructively
  • collaboration

Nice to have

  • JavaScript
  • TypeScript
  • another relevant language

What the JD emphasized

  • demonstrated track record of designing, building, and delivering AI or machine-learning systems in enterprise environments, including taking systems from prototype to production
  • substantial personal contributions in code, architecture, evaluation, debugging, or production engineering

Other signals

  • customer-facing
  • production deployments
  • AI systems
  • business outcomes
  • technical judgment
  • hands-on engineering
  • customer leadership
  • prototyping
  • evaluation
  • production launch
  • scale
  • model behavior
  • reliability
  • latency
  • cost
  • safety
  • security
  • governance
  • operational readiness
  • sustained adoption
  • meaningful customer impact
  • frontier of AI
  • product evolution
  • enterprise environments
  • prototype to production
  • Python
  • AI application stack
  • evaluate AI systems
  • representative data
  • graders
  • production signals
  • human judgment
  • enterprise production requirements
  • integrations
  • reliability
  • observability
  • security
  • privacy
  • data governance
  • performance
  • cost
  • customer workflows
  • adoption
  • measurable business outcomes
  • clarity and credibility
  • hands-on engineers
  • technical leaders
  • security teams
  • product leaders
  • executives
  • high agency
  • strong technical judgment
  • end-to-end ownership
  • ambiguous environments
  • learn quickly
  • challenge assumptions constructively
  • collaborate with humility