Forward Deployed Engineer, AI and Agentic SDLC at GitLab

Role overview

GitLab is the intelligent orchestration platform for DevSecOps. GitLab enables organizations to increase developer productivity, improve operational efficiency, reduce security and compliance risk, and accelerate digital transformation. More than 50 million registered users and more than 50% of the Fortune 100* trust GitLab to ship better, more secure software faster.

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. GitLab is where careers accelerate, innovation flourishes, and every voice is valued. Our high-performance culture is driven by our values and continuous knowledge exchange, enabling our team members to reach their full potential while collaborating with industry leaders to solve complex problems. Co-create the future with us as we build technology that transforms how the world develops software.

Fortune 500® is a registered trademark of Fortune Media IP Limited, used under license. Claim based on GitLab data. Fortune 100 refers to the top 20% ranked companies in the 2025 Fortune 500 list, published in June 2025. Fortune and Fortune Media IP Limited are not affiliated with, and do not endorse products or services of GitLab.

Forward Deployed Engineer, AI and Agentic SDLC

GitLab’s Forward Deployed Engineering team closes the gap between what strategic customers need and what GitLab can currently deliver. We work alongside customers, Product, Engineering, and existing field teams, using hands-on engineering to resolve product friction, accelerate customer outcomes, and contribute reusable improvements back to GitLab.

As a Staff Forward Deployed Engineer focused on AI and Agentic SDLC, you will help define and build GitLab Duo Agent Platform at the frontier of agentic software development. You will design agent workflows and context systems, build integrations and product changes, evaluate and optimize system behavior, and work directly with customers to understand where AI systems succeed, fail, or create friction.

Engagements may range from a focused technical consultation to a long-running design partnership. The technical work may span GitLab’s CLI, IDE integrations, AI Gateway, web experiences, agent orchestration, evaluation systems, authorization controls, and other evolving parts of the platform.

GitLab is proud to be an equal opportunity workplace and is an affirmative action employer. GitLab’s policies and practices relating to recruitment, employment, career development and advancement, promotion, and retirement are based solely on merit, regardless of race, color, religion, ancestry, sex (including pregnancy, lactation, sexual orientation, gender identity, or gender expression), national origin, age, citizenship, marital status, mental or physical disability, genetic information (including family medical history), discharge status from the military, protected veteran status (which includes disabled veterans, recently separated veterans, active duty wartime or campaign badge veterans, and Armed Forces service medal veterans), or any other basis protected by law. GitLab will not tolerate discrimination or harassment based on any of these characteristics. See also GitLab’s EEO Policy and EEO is the Law . If you have a disability or special need that requires accommodation , please let us know during the recruiting process .

Responsibilities

• Deliver technical work for AI and agentic software delivery for customers.
• Collaborate with product, engineering, security and customer-facing teams.
• Improve reliability, automation, testing, observability and documentation.
• Translate requirements into maintainable software and operational outcomes.

Qualifications

• 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.
• 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.

Technology in this role

• Python: Python TypeScript Java C# AWS Azure GCP Kubernetes .
• TypeScript: Python TypeScript Java C# AWS Azure GCP Kubernetes .
• Java: Python TypeScript Java C# AWS Azure GCP Kubernetes .
• C#: Python TypeScript Java C# AWS Azure GCP Kubernetes .
• AWS: Python TypeScript Java C# AWS Azure GCP Kubernetes .
• Azure: Python TypeScript Java C# AWS Azure GCP Kubernetes .
• GCP: Python TypeScript Java C# AWS Azure GCP Kubernetes .
• Kubernetes: Python TypeScript Java C# AWS Azure GCP Kubernetes .
• Go: Python TypeScript Java C# AWS Azure GCP Kubernetes .
• Kotlin: Strong programming skills in Python, TypeScript, Go, Rust, Java, Kotlin, Ruby, C#, or another language used to build AI, backend, developer-tooling, or platform .
• C: Python TypeScript Java C# AWS Azure GCP Kubernetes .
• Rust: Strong programming skills in Python, TypeScript, Go, Rust, Java, Kotlin, Ruby, C#, or another language used to build AI, backend, developer-tooling, or platform .
• Ruby on Rails: • Experience with Ruby on Rails or other parts of GitLab’s existing product .
• Ruby: Strong programming skills in Python, TypeScript, Go, Rust, Java, Kotlin, Ruby, C#, or another language used to build AI, backend, developer-tooling, or platform .
• RAG: • Experience with LangChain, LangGraph, RAG, cache-augmented generation, knowledge graphs, agent evaluation, tracing, or related .
• LangChain: • Experience with LangChain, LangGraph, RAG, cache-augmented generation, knowledge graphs, agent evaluation, tracing, or related .

Benefits

• Equity Compensation & Employee Stock Purchase Plan

About GitLab

The Platform Readiness team is part of GitLab Developer Experience, building internal developer tooling capability for platform readiness across GitLab's four deployment targets: Self-Managed, Dedicated, Dedicated for Government, and GitLab.com. We partner with platform teams, feature teams, and product management to ensure every feature is evaluated against platform readiness requirements before it ships.

Current challenges we're tackling:

Moving from manual coordination to embedded automation: Transitioning from manual, ad-hoc platform readiness assessments to automated, agentic evaluation while maintaining human oversight and trust

Building a structured, machine-readable requirements body: Evolving the platform requirements schema to capture the full scope of platform readiness concerns across all domains and platforms

Making platform readiness native to the tools feature teams already use: Enabling platform teams to contribute domain expertise and validation logic without requiring deep Platform Readiness infrastructure knowledge

Building the measurement systems to know we're working: Creating observability and evaluation frameworks that give us confidence in agentic-workflow decisions and surface failures early

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