Forward Deployed Engineer

GitLab GitLab · Enterprise · United States · Customer Experience

Staff Forward Deployed Engineer role focused on helping enterprise customers adopt GitLab Duo Agent Platform in complex, regulated environments. This role involves deep technical discovery, designing adoption paths, and building reusable solutions for AI-enabled workflows, influencing product direction based on field needs.

What you'd actually do

  1. Conduct deep technical discovery in selected strategic accounts to assess platform readiness, evaluate constraints, and identify high-value adoption opportunities across GitLab and GitLab Duo Agent Platform.
  2. Lead architecture and delivery design for complex enterprise environments where platform migration, regulated requirements, and product boundaries intersect.
  3. Partner with customer stakeholders and GitLab account teams to prioritize use cases based on business impact, technical feasibility, repeatability, and long-term platform value.
  4. Design and build bounded proofs, prototypes, deployment patterns, and reusable accelerators across source code management, CI/CD, security, compliance, and AI-enabled workflows.
  5. Architect self-managed and enterprise deployments, including runners, access controls, network boundaries, observability, AI Gateway, model connectivity, and governance controls.

Skills

Required

  • 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 GitLab CI/CD, pipeline design, YAML, runners, and GitLab 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 track record of creating reusable technical assets that outlive a single engagement, along with strong written and verbal communication skills for technical design and architecture guidance.
  • Comfort leading conversations with senior stakeholders across security, compliance, engineering, platform, and business teams, especially in ambiguous enterprise environments.

What the JD emphasized

  • regulated requirements
  • reusable solutions
  • AI-enabled workflows
  • AI Gateway
  • model connectivity

Other signals

  • customer adoption of AI platforms
  • building reusable solutions for AI workflows
  • architecting AI Gateway and model connectivity