Applied AI Engineer, Enterprise - Healthcare Life Sciences (HLS)

Across industries, OpenAI's Applied AI Engineering (AIE) team helps organizations turn frontier AI into safe, reliable production systems that deliver meaningful business outcomes.

As an Enterprise AIE, you will serve as a technical partner to leading organizations within the Healthcare and Life Sciences segment (HLS), helping them turn ambitious AI use cases into durable production systems and adoption at scale.

In this role, you will:

• 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.
• Partner with customer security, privacy, clinical and business teams to design responsible AI deployments, including data handling, access controls, evaluation, human oversight and monitoring.
• Create reusable architectures, tooling, playbooks, and technical guidance that accelerate future enterprise deployments.

You might thrive in this role if you:

• 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.
• 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.
• Have experience delivering AI or software in healthcare, life sciences or similarly complex regulated enterprises, with measurable outcomes beyond a proof of concept and familiarity with sensitive data, legacy systems and interoperability.
• 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.
• Healthcare or life sciences experience is strongly preferred; strong candidates from other regulated enterprise environments are also encouraged to apply.