AI Engineer

GitLab is seeking an AI Engineer to help build AI-powered solutions that drive business outcomes, initially focusing on Sales, Marketing, and Customer Support. The role involves understanding workflows, owning initiatives end-to-end, and integrating AI into key systems in a remote, values-driven environment.

Location: Remote, US

Salary: $108,400—$129,600 USD

Responsibilities

• Diagnose business problems before building solutions. Map workflows, identify constraints, and confirm whether AI is the right intervention. Be prepared to say "this doesn't need AI" when that's the honest answer.
• Own AI initiatives end-to-end, from stakeholder discovery and technical design through implementation, deployment, and iteration.
• Design, develop, and ship AI-powered solutions quickly, 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 right tool wins, whether that's custom code, a platform, or a well-crafted prompt.
• Be Customer Zero: leverage and showcase GitLab's AI offerings wherever possible, feeding real-world usage insights back to R&D.
• Partner closely with stakeholders across functions to understand the real constraints. Ask the right questions, bridge technical and non-technical perspectives, and align on outcomes before jumping to solutions.
• Define and track success through business metrics, flow metrics, and feedback loops that make performance visible and actionable.
• Contribute to technical direction by evaluating tools, documenting patterns, and creating reusable foundations that help the team scale its impact.

Requirements

• Genuinely invested in technology, the foundational and the cutting-edge in equal measure. You're as energised by a well-designed API integration as you are by the latest foundation model release. You reach for the simplest solution that solves the problem well, rather than forcing new technology when proven approaches would do. AI is a powerful part of your toolkit, but it sits on top of solid engineering fundamentals, not in place of them.
• You can build working solutions end-to-end, write clean and maintainable code, and debug effectively. Whether your skills were honed in a traditional engineering role, through building automations, or shipping side projects, what matters is that you can deliver production-quality work independently.
• 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 iterating systematically on prompt design.
• 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.
• 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 reach for and when.
• You think critically about how the solutions you build could be exploited, misused, or produce unintended consequences. You know how to design appropriate guardrails (input validation, output filtering, access controls, prompt injection defences, and data leakage prevention) and you treat these as first-class engineering concerns.
• The ability to look at a complex process and see the constraint. Comfortable mapping how work flows end-to-end, 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?"
• Familiarity with the landscape of enterprise business systems, CRM (Salesforce), marketing automation (Marketo), support platforms (Zendesk), integration and orchestration tools (Workato), AI platforms (Relevance AI), and enterprise search and knowledge tools (Glean). You don't need deep experience with all of these, but to understand what they do, how they fit together, and be willing to build with and across them. A strong understanding of enterprise data models and workflows is essential.
• Ability to have meaningful conversations with stakeholders across diverse domains and quickly understand their unique needs.
• Track record of owning complex initiatives from discovery through delivery. Comfortable operating with ambiguity and driving to measurable outcomes independently.
• Ability to scope MVPs, prioritise ruthlessly, and deliver iteratively. In addition, consider adoption, user experience, and business outcomes.