Applied AI Engineer, Enterprise - EDU

OpenAI’s Applied AI Engineering team partners with universities and other educational institutions to turn ambitious AI use cases into reliable production systems and adoption at scale. The Enterprise AIE provides hands-on engineering and technical leadership for customer deployments, while sharing deployment insights to inform OpenAI products.

Responsibilities:

• Work hands-on in code to design, build, evaluate, and deploy AI systems from prototype to reliable production and scaled adoption that solve important customer problems and deliver measurable business outcomes
• Partner with enterprise customers to identify high-value opportunities and translate them into technical architectures, implementation plans, evaluation strategies, and success criteria
• Make sound technical decisions across model selection, agent and tool design, retrieval, context design, data pipelines, evaluation, reliability, observability, latency, cost, safety, security, and governance
• Diagnose complex implementation challenges, reproduce failures, test hypotheses, and drive blockers to resolution
• Partner closely with customers’ engineering teams and OpenAI’s Product, Research, Engineering, Security, and Go-to-Market teams to turn deployment experience into actionable product feedback
• Create reusable architectures, tooling, playbooks, and technical guidance that accelerate future enterprise deployments

Requirements:

• Have personally designed, built, and shipped AI or machine-learning systems in enterprise environments, taking them from prototype to production and making substantial contributions through coding, architecture, evaluation, debugging, or production engineering
• Relevant backgrounds may include applied AI or ML engineering, forward-deployed engineering, software engineering, customer engineering, solutions architecture, or technical consulting
• Are highly proficient in Python and comfortable working end to end across model APIs, agent and tool development, retrieval and data pipelines, evaluation, observability, integrations, and production operations
• Know how to evaluate AI systems systematically using representative data, automated graders, production signals, and human judgment
• Have navigated enterprise production requirements, including integrations, reliability, observability, security, privacy, data governance, performance, and cost
• Can connect technical decisions to customer workflows, adoption, and measurable business outcomes
• Communicate clearly and credibly with hands-on engineers, technical leaders, security and product teams, and executives
• Take end-to-end ownership, exercise strong technical judgment, and move effectively through ambiguity
• Learn quickly, challenge assumptions constructively, and collaborate with humility
• Experience with JavaScript, TypeScript, or another relevant language is valuable
• Experience navigating education or research environments, including institutional data, privacy, access controls and complex integrations, is valuable
• Experience working with education institutions, particularly universities, is preferred but not required