Applied AI Engineer, Enterprise
Remote • São Paulo • FullTime
Posted 1mo ago
Remote Work Policy
Fully remote
Employment Type
FullTime
Categories
Applied AI Engineer
About the job
As an Enterprise Applied AI Engineer at OpenAI, 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. This role involves writing and debugging code, building evaluation systems, resolving complex integrations, and guiding decisions across model behavior, reliability, latency, cost, safety, security, governance, and operational readiness. Success is measured by production systems, sustained adoption, and meaningful customer impact. 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.
Responsibilities
- Partner with enterprise customers to identify high-value opportunities and translate them into technical architectures, implementation plans, evaluation strategies, and success criteria.
- Design, build, and deploy AI systems that solve customer problems and produce measurable business outcomes.
- Build prototypes, evaluation harnesses, reference implementations, integrations, and production accelerators.
- Make sound technical decisions regarding 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 prototypes to reliable production systems, sustained adoption, and scaled impact.
- Partner with customer engineering teams and OpenAI Product, Research, Engineering, Security, and go-to-market teams, translating deployment experience into product feedback.
- Create reusable architectures, tooling, playbooks, and technical guidance to accelerate future enterprise deployments.
Requirements
- Demonstrated track record of designing, building, and delivering AI or machine-learning systems in enterprise environments, from prototype to production.
- Substantial personal contributions in code, architecture, evaluation, debugging, or production engineering.
- High proficiency in Python and comfort working across an AI application stack.
- Experience evaluating 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.
- Clear and credible communication 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.
- Quick learning, constructive challenging of assumptions, and collaborative approach.