Forward Deployed Engineer - UK

Git Lab is the intelligent orchestration platform for Dev Sec Ops . Git Lab enables organizations to increase developer productivity, improve operational efficiency, reduce security and compliance risk, and accelerate digital transformation.

The same principles built into our products are reflected in how our team works: we embrace AI as a core productivity multiplier, with all team members expected to incorporate AI into their daily workflows to drive efficiency, innovation, and impact.

* Fortune 500® is a registered trademark of Fortune Media IP Limited, used under license. Claim based on Git Lab data. Fortune 100 refers to the top 20% ranked companies in the 2025 Fortune 500 list, published in June 2025.

As a Staff Forward Deployed Engineer at Git Lab, you will work at the intersection of customer outcomes, product direction, and hands‑on engineering. This role is focused on strategic accounts in the APJ region, where you help customers adopt Git Lab and the Git Lab Duo Agent Platform in complex enterprise environments, including self‑managed, regulated, and constrained deployments. You will guide deep technical discovery, design practical adoption paths, and build reusable solutions that help customers move from early platform use into broader CI/CD, security, compliance, and AI-enabled workflows.

Some examples of projects you will work on:

• Building a regulated-environment deployment accelerator for Git Lab Duo Agent Platform in self-managed or constrained environments
• Authoring reusable reference architectures for AI Gateway, identity, runners, network boundaries, model connectivity, and governance controls
• Identifying a geo‑replication gap in a self‑managed deployment, tracing it to a root cause in Gitaly or the git backend, and contributing a targeted code or design change upstream

• Conduct deep technical discovery in selected strategic accounts to assess platform readiness, evaluate constraints, and identify high‑value adoption opportunities across Git Lab and Git Lab Duo Agent Platform.
• Lead architecture and delivery design for complex enterprise environments where platform migration, regulated requirements, and product boundaries intersect.
• Partner with customer stakeholders and Git Lab account teams to prioritize use cases based on business impact, technical feasibility, repeatability, and long‑term platform value.
• Design and build bounded proofs, prototypes, deployment patterns, and reusable accelerators across source code management, CI/CD, security, compliance, and AI-enabled workflows.
• Architect self‑managed and enterprise deployments, including runners, access controls, network boundaries, observability, AI Gateway, model connectivity, and governance controls.
• Turn recurring field patterns into reusable assets such as runbooks, templates, design notes, technical guidance, product briefs, and reference architectures that can be used across accounts.
• Contribute code, technical designs, or architecture changes when strategically necessary, in partnership with product and engineering, to address blockers that should be solved upstream.
• Travel as needed for strategic customer engagements, architecture workshops, and team coordination, with expected travel up to 50%.

• Experience in software engineering, platform architecture, forward deployed engineering, technical consulting, or similar customer‑facing engineering roles.
• Strong software engineering fundamentals, including the ability to read, reason about, and contribute to production systems, ideally with experience in Ruby on Rails and/or Go.
• Strong systems design and software architecture skills, with experience evaluating APIs, asynchronous workflows, CI/CD systems, security boundaries, scalability, and operational tradeoffs.
• Hands‑on experience with Git Lab CI/CD, pipeline design, YAML, runners, and Git Lab APIs.
• Experience with infrastructure as code and enterprise deployment tooling such as Terraform, Ansible, Helm, or similar approaches.
• Working knowledge of large language models, agentic patterns, tool orchestration, and the practical limits of AI systems in production environments.
• A…