AI Engineer at GitLab

An Overview of This Role

As an AI Engineer at GitLab, you will play a pivotal role in shaping GitLab's transformation into an AI-first company. Reporting to the Director, Enterprise AI, this is a hands-on technical leadership position focused on delivering internal AI-powered solutions that drive measurable business outcomes. The emphasis is on building solutions quickly, but critically, also on understanding the underlying business problem, mapping workflows, identifying constraints, and validating if AI is the correct solution before development begins. You will own initiatives from discovery through deployment, combining strong engineering skills with systems thinking and business acumen. Your initial focus will be on Sales, Marketing, and Customer Support, embedding AI solutions into core systems and workflows. This role offers the unique opportunity to influence how GitLab team members operate, improve organizational flow, and advance GitLab's mission in a remote, asynchronous, and values-driven environment.

What You Will Do

• Diagnose business problems before building solutions. Map workflows, identify constraints, and confirm whether AI is the right intervention. Be prepared to state when AI is not needed.
• Own AI initiatives end to end, from stakeholder discovery and technical design through implementation, deployment, and iteration.
• Design, develop, and ship AI-powered solutions rapidly, delivering working prototypes in days, not months, with a focus on practical outcomes and measurable business value.
• Improve organizational flow by building solutions that reduce bottlenecks, shorten lead times, and increase throughput. Measure success using flow metrics alongside adoption and ROI.
• Integrate AI capabilities into existing systems and workflows using APIs, orchestration tools, and modern AI platforms, including GitLab Duo Agent Platform where appropriate. The best tool for the job should always be chosen, whether custom code, a platform, or a well-crafted prompt.
• Act as Customer Zero: leverage and showcase GitLab's AI offerings wherever possible, providing real-world usage insights back to R&D.
• Partner closely with stakeholders across functions to understand real constraints. Ask pertinent questions, bridge technical and non-technical perspectives, and align on outcomes before developing solutions.
• Define and track success through business metrics, flow metrics, and feedback loops that ensure visibility and actionability of performance.
• Contribute to technical direction by evaluating tools, documenting patterns, and creating reusable foundations to help the team scale its impact.

What You Will Bring

• A Technologist at Heart: Genuinely invested in both foundational and cutting-edge technology. You are energized by a well designed API integration as much as the latest foundation model release. You favor the simplest solution that effectively solves the problem over forcing new technology. AI is a powerful tool in your kit, but it complements, rather than replaces, solid engineering fundamentals.
• Competent, Confident Coding Skills: Ability to build working solutions end to end, write clean and maintainable code, and debug effectively. Production quality work can be delivered independently, whether skills were honed in traditional engineering roles, through automations, or side projects.
• AI & LLM Technical Depth: Strong proficiency in at least one modern scripting language (Python, JavaScript/TypeScript, or similar) and a solid understanding of REST APIs, GraphQL, and integration patterns. Deep, practical experience with modern AI technologies, specifically:
  • Prompt Engineering as a Core Discipline: Designing effective system prompts, managing context windows, structuring multi turn interactions, evaluating output quality, and systematically iterating on prompt design.
  • Model Selection and Cost Performance Trade Offs: Understanding when a smaller fine tuned model outperforms a general purpose large one, when RAG is the right architecture versus expanding the context window, and how to make principled decisions about capability versus cost.
  • Agentic Architecture Patterns: Tool use, multi agent orchestration, human in the loop designs, guardrails, evaluation frameworks, and production grade reliability patterns.
  • Practical Fluency Across the LLM Ecosystem: Hands on experience with models from Anthropic, OpenAI, open source alternatives, and the judgment to know which to use when.
• AI Safety & Risk Awareness: Critical thinking about how built solutions could be exploited, misused, or produce unintended consequences. Knowledge of how to design appropriate guardrails (input validation, output filtering, access controls, prompt injection defenses, and data leakage prevention), treating these as first class engineering concerns.
• Systems Thinking & Diagnostic Rigor: The ability to analyze a complex process and identify the constraint. Comfortable mapping end to end workflows, identifying bottlenecks, and tracing problems to root causes before proposing solutions. You instinctively ask “what’s actually blocking flow here?” before asking “what model should I use?”
• Business System Expertise: Familiarity with enterprise business systems, including CRM (Salesforce), marketing automation (Marketo), support platforms (Zendesk), integration and orchestration tools (Workato), AI platforms (Relevance AI), and enterprise search and knowledge tools (Glean). Deep experience with all is not required, but understanding their function, integration, and willingness to build with them is essential. A strong understanding of enterprise data models and workflows is crucial.
• Broad Functional Understanding: Ability to engage in meaningful conversations with stakeholders across diverse domains and quickly grasp their unique needs.
• End to End Ownership: Proven track record of owning complex initiatives from discovery through delivery. Comfortable operating with ambiguity and independently driving to measurable outcomes.
• Product Mindset: Ability to scope MVPs, prioritize ruthlessly, and deliver iteratively. Consideration for adoption, user experience, and business outcomes is key.

Preferred Requirements

• Experience with GitLab platform and CI/CD workflows.
• Background in consulting, solutions engineering, or customer facing technical roles.
• Familiarity with value stream mapping, flow metrics, or Theory of Constraints thinking.
• Experience with low code/no code orchestration tools (n8n, Make, Workato) alongside custom development.
• Previous startup or high growth company experience.
• Experience mentoring or leading technical projects with junior engineers.

About the Team

You will join the Enterprise Technology & AI team. We are the backbone of the organization, driving transformation in how GitLab team members make decisions, operate at scale, and deliver results for our customers. We believe the best AI solutions start with understanding the system, not the technology. We value individuals who think in terms of constraints and flow, who build with conviction, and who are continuous learners. We operate in an all remote, asynchronous setting, guided by GitLab's values of collaboration, results, efficiency, diversity, inclusion and belonging, iteration, and transparency.

How GitLab Supports Full Time Employees

• Benefits to support your health, finances, and well being.
• Flexible Paid Time Off.
• Team Member Resource Groups.
• Equity Compensation & Employee Stock Purchase Plan.
• Growth and Development Fund.
• Parental Leave.

Please note that we welcome interest from candidates with varying levels of experience; many successful candidates do not meet every single requirement. Additionally, studies have shown that people from underrepresented groups are less likely to apply to a job unless they meet every single qualification. If you're excited about this role, please apply and allow our recruiters to assess your application.

Country Hiring Guidelines: GitLab hires new team members in countries around the world. All of our roles are remote, however some roles may carry specific location based eligibility requirements. Our Talent Acquisition team can help answer any questions about location after starting the recruiting process.