AI Deployment Engineer, Enterprise

OpenAI•San Francisco, CA

1d•$197,000 - $278,000•Hybrid

About The Position

OpenAI’s AI Deployment Engineering team helps organizations turn frontier AI capabilities into safe, reliable, and high-impact production systems. We work with customer executives, product and engineering teams, security leaders, and transformation teams to identify valuable opportunities, accelerate technical implementation, and scale what works. Enterprise deployments are defined by complexity rather than any one industry: existing architectures, diverse data environments, security and governance requirements, multiple stakeholder groups, and organization-wide change. We turn lessons from these deployments into better products and reusable patterns for customers everywhere. As an Enterprise AI Deployment Engineer, you will partner directly with leading organizations to design, build, and deploy AI systems that deliver measurable business outcomes. You will combine deep technical judgment, hands-on engineering, and customer leadership to take ambitious ideas from use-case selection and architecture through prototyping, evaluation, production launch, and scale. You will write and debug code, build evaluation systems, resolve complex integrations, and guide decisions involving model behavior, reliability, latency, cost, safety, security, governance, and operational readiness. Success is measured by production systems, sustained adoption, and meaningful customer impact—not simply activity or successful demonstrations. This is a rare opportunity to work on consequential real-world deployments at the frontier of AI while directly influencing how OpenAI’s products evolve. This role is based in our SF or NYC office. We use a hybrid work model of 3 days in the office per week and offer relocation assistance to new employees.

Requirements

Have a demonstrated track record of designing, building, and delivering AI or machine-learning systems in enterprise environments, including taking systems from prototype to production. Relevant backgrounds may include applied AI or ML engineering, forward-deployed engineering, software engineering, customer engineering, solutions architecture, or technical consulting.
Can point to substantial personal contributions in code, architecture, evaluation, debugging, or production engineering—not only program or stakeholder management.
Are highly proficient in Python and comfortable working across an AI application stack; experience with JavaScript, TypeScript, or another relevant language is valuable.
Understand how to evaluate AI systems systematically using representative data, graders, production signals, and human judgment.
Have navigated enterprise production requirements such as integrations, reliability, observability, security, privacy, data governance, performance, and cost.
Can connect technical decisions to customer workflows, adoption, and measurable business outcomes.
Communicate with clarity and credibility across hands-on engineers, technical leaders, security teams, product leaders, and executives.
Bring high agency, strong technical judgment, and end-to-end ownership in ambiguous environments.
Learn quickly, challenge assumptions constructively, and collaborate with humility; experience in a particular industry or with OpenAI products is not required.

Responsibilities

Partner directly with enterprise customers to identify high-value opportunities and translate them into technical architectures, implementation plans, evaluation strategies, and measurable success criteria.
Design, build, and deploy AI systems that solve important customer problems and produce measurable business outcomes.
Work hands-on in code to build prototypes, evaluation harnesses, reference implementations, integrations, and production accelerators.
Make sound technical decisions across models, agents, retrieval, tools, data, reliability, observability, latency, cost, safety, security, and governance.
Diagnose complex implementation challenges, reproduce failures, test hypotheses, and drive blockers toward resolution.
Help customers progress from promising prototypes to reliable production systems, sustained adoption, and scaled impact.
Partner closely with customer engineering teams and OpenAI Product, Research, Engineering, Security, and go-to-market teams, translating deployment experience into high-signal product feedback.
Create reusable architectures, tooling, playbooks, and technical guidance that accelerate future enterprise deployments.