AI Deployment Engineer, Enterprise

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.

Requirements

• 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.
• Substantial personal contributions in code, architecture, evaluation, debugging, or production engineering.
• Highly proficient in Python and comfortable working across an AI application stack.
• Understand how to evaluate AI systems systematically using representative data, graders, production signals, and human judgment.
• Experience navigating enterprise production requirements such as integrations, reliability, observability, security, privacy, data governance, performance, and cost.
• Ability to 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.
• High agency, strong technical judgment, and end-to-end ownership in ambiguous environments.
• Learn quickly, challenge assumptions constructively, and collaborate with humility.

Nice-to-Haves

• Experience with JavaScript, TypeScript, or another relevant language.
• Experience in a particular industry or with OpenAI products.