AI Engineer

As an AI Engineer at GitLab, you will help build the foundation for GitLab's transformation into an AI-first company. Reporting to the Director, Enterprise AI, you will be a hands-on technical leader delivering internal AI-powered solutions that drive measurable business outcomes. The role starts with understanding the real problem: mapping how work moves across teams, tools, and handoffs, identifying the true constraint, and validating whether AI is the right solution before development begins. Your initial focus spans Sales, Marketing, and Customer Support, embedding AI solutions into key systems and workflows.

Responsibilities

• Diagnose business problems before building solutions: map workflows, identify constraints, and confirm whether AI is the right intervention, saying so honestly when it is not.
• Own AI initiatives end-to-end, from stakeholder discovery and technical design through implementation, deployment, and iteration.
• Ship AI-powered solutions quickly, delivering working prototypes in days, 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, measured with flow metrics alongside adoption and ROI.
• Integrate AI capabilities into existing systems using APIs, orchestration tools, and modern AI platforms, including the GitLab Duo Agent Platform where appropriate.
• Be Customer Zero: leverage and showcase GitLab's AI offerings, feeding real-world usage insights back to R&D.
• Define and track success through business metrics, flow metrics, and feedback loops that make performance visible and actionable.

Qualifications

• Competent, confident coding skills: you build working solutions end-to-end, write clean, maintainable code, and debug effectively.
• Strong proficiency in at least one modern scripting language (Python, JavaScript/TypeScript, or similar) plus a solid understanding of REST APIs, GraphQL, and integration patterns.
• Deep practical experience with modern AI technologies: prompt engineering as a core discipline, model selection and cost-performance tradeoffs, RAG versus context window decisions, and agentic architecture patterns including tool use, multi-agent orchestration, human-in-the-loop designs, and guardrails.
• AI safety and risk awareness: input validation, output filtering, access controls, prompt injection defences, and data leakage prevention treated as first-class engineering concerns.
• Systems thinking and diagnostic rigour: the ability to map how work flows end-to-end and trace problems to root causes before proposing solutions.
• Familiarity with enterprise business systems such as CRM, marketing automation, support platforms, and orchestration tools.