About the Company
The OpenAI Deployment Company ("DeployCo") is the deployment arm of OpenAI, purpose-built to deliver enterprise AI transformation at scale. As model capabilities accelerate, deployment has become the primary constraint to realizing AI value creation within large enterprises. DeployCo bridges that gap.
Our ambition is to build the company that defines AI deployment. We operate at the intersection of cutting-edge AI, enterprise transformation, and scaled services delivery. We partner with enterprise customers to identify where AI can transform their businesses and build solutions leveraging OpenAI's models and products. We work with many of the world's largest and most influential companies to solve their most complex business challenges using the latest advances in AI.
People who thrive at DeployCo get excited about ambitious and complex work. We are deep believers in the future with AI. We execute quickly, value intellectual honesty and empathy, and obsess over the solutions we build for our customers.
DeployCo is being built from the ground up as a subsidiary of OpenAI and funded with a partnership of outside investors.
About the Role
We are seeking a Forward Deployed Engineer to lead the design, build, and deployment of enterprise AI systems. In this role, you will operate as a senior technical owner across customer engagements, shaping architecture, setting engineering standards, and helping teams move from prototypes to durable production AI systems. DeployCo is one of the few places where engineers can work with the autonomy of a small startup while building for the scale, complexity, and impact of the world’s largest enterprises.
Your day-to-day will include leading technical discovery, designing solution architecture, building production-grade agentic AI applications, mentoring engineers, evaluating model behavior, and translating field learning into reusable platform and delivery patterns. We typically use existing closed- and open-source foundation models, including OpenAI models and APIs, and focus on turning state-of-the-art capabilities into reliable products.
We're looking for engineers who deploy high-quality agentic systems into real customer workflows and business critical decisions and deliver reusable components that help future deployments move faster.
This role is based in the UK, with hybrid work and customer travel shaped by deployment needs.
In this role, you will:
Lead software engineering workstreams for customer deployments, owning architecture, implementation quality, evaluation strategy, and production readiness for LLM-powered systems.
Build AI applications, agents, and workflow tools using LLMs, retrieval, orchestration, APIs, data systems, and production software practices.
Guide engineers through ambiguous technical problems, reviewing designs, code, tradeoffs, and operating assumptions.
Work directly with customer technical teams and business stakeholders to translate workflows into reliable AI systems.
Prototype quickly, identify what is reusable, and harden successful patterns into reference architectures and implementation assets.
Design evaluation and monitoring approaches that improve quality, reliability, safety, and customer trust.
Surface field insights to Product and Research, connecting deployment reality to roadmap priorities and model/system improvements.
You may thrive in this role if you:
Bring senior software engineering experience building production applications and microservices, including APIs, scalable data pipelines, data systems, and cloud infrastructure.
Use your expertise in Python and SQL to design and build robust data applications in enterprise environments.
Bring production LLM experience, including leading teams delivering generative AI systems with hands-on understanding in agents, retrieval, evaluation, fine-tuning, prompt engineering, and model integration.
Have led software engineering projects from prototype to production and can set a high-quality bar while keeping teams moving quickly.
Are comfortable as a hands-on player-coach: writing code, reviewing designs, mentoring engineers, and owning customer outcomes.
Communicate clearly with executive, product, research, customer, and engineering audiences when decisions involve risk or ambiguity.
Make disciplined standardization-versus-customization decisions instead of defaulting to bespoke work.
Operate calmly under pressure and turn messy deployment problems into practical, reusable engineering patterns.
