Forward Deployed Engineer, AI and Agentic SDLC

About the Company

Responsibilities

• ● Design and build agentic systems using models, tools, action loops, orchestration, context, retrieval, state, memory, evaluation, tracing, and human oversight.
• ● Develop context and knowledge systems using approaches such as retrieval-augmented generation, cache-augmented generation, graph-based retrieval, repository understanding, and context optimization.
• ● Work directly in strategic customer environments to understand technical friction and turn ambiguous needs into working solutions.
• ● Build agent workflows, integrations, evaluation suites, reference architectures, and product capabilities that improve customer outcomes.
• ● Contribute production-quality changes across GitLab Duo Agent Platform and the broader GitLab product.
• ● Evaluate and improve system quality, reliability, latency, cost, token use, safety, authorization, governance, and developer experience.
• ● Diagnose failures across models, context, retrieval, tool use, control flow, product behavior, permissions, and surrounding software-development workflows.
• ● Partner with Product and Engineering as an extension of their teams, accelerating work while aligning with product direction and engineering standards.
• ● Generalize customer-specific learning into reusable platform capabilities, implementation patterns, and technical guidance.
• ● Provide Staff-level technical leadership through architecture, implementation, review, mentorship, and influence across teams.

Requirements

• ● Staff-level software-engineering experience building, shipping, and improving complex production systems.
• ● Hands-on experience designing and building substantive AI systems, with the ability to evaluate, trace, debug, and optimize their behavior.
• ● Strong understanding of how agentic systems use models, context, retrieval, tools, state, control flow, evaluation, and human oversight.
• ● Understanding of why AI systems become unreliable, fail to achieve adoption, or consume significant resources without producing useful outcomes.
• ● Depth in one or more areas of AI engineering, such as agent systems, context and knowledge systems, AI developer tools, inference, or AI-enabled product engineering.
• ● Strong programming skills in Python, TypeScript, Go, Rust, Java, Kotlin, Ruby, C, or another language used to build AI, backend, developer-tooling, or platform systems.
• ● Ability to learn and contribute across large, mature, polyglot codebases.
• ● Practical understanding of AI safety, authorization, governance, observability, and cost management.
• ● Ability to communicate clearly with customers and senior stakeholders, work through ambiguity, and build trust while remaining deeply technical.
• ● A record of Staff-level impact through technical direction, reusable systems, mentorship, cross-team influence, or product architecture.
• ● AI engineering spans multiple disciplines, and we do not expect every candidate to have equal depth across all of them. Strong candidates will bring deep expertise in one or more areas, along with the systems perspective needed to collaborate across the complete platform.
• ● It Would Be Helpful If You Have
• ● Familiarity with Git, GitLab, CI/CD, developer platforms, or large open-source products.
• ● Public technical writing, architecture guidance, open-source contributions, talks, or other evidence of technical leadership.
• ● How GitLab Supports Full-Time Employees
• ● Flexible Paid Time Off
• ● Team Member Resource Groups
• ● Equity Compensation & Employee Stock Purchase Plan
• ● Growth and Development Fund
• ● Parental Leave