Senior AI Engineer

GitLab

4d•$139,200 - $218,400•Remote

About The Position

As a Senior AI Engineer at GitLab, you'll help build the foundation for GitLab's transformation into an AI-first company. Reporting to the Director, Enterprise AI, you'll be a hands-on technical leader responsible for delivering internal AI-powered solutions that drive measurable business outcomes. Building fast matters, but it's not enough on its own. This 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 you begin development. From there, you'll take ownership from discovery through deployment, combining strong engineering skills with systems thinking and business understanding. Your initial focus will span Sales, Marketing, and Customer Support, where you will embed AI solutions into key systems and workflows. This role offers the opportunity to shape how GitLab team members work, improve flow across the organization, and help advance our mission in a remote, asynchronous, and values-driven environment.

Requirements

A Technologist at Heart - 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.
Competent, Confident Coding Skills - 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.
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 iterating systematically 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 reach for and when.
AI Safety & Risk Awareness - 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.
Systems Thinking & Diagnostic Rigour - 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?"
Business System Expertise - 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.
Broad Functional Understanding - Ability to have meaningful conversations with stakeholders across diverse domains and quickly understand their unique needs.
End-to-End Ownership - Track record of owning complex initiatives from discovery through delivery. Comfortable operating with ambiguity and driving to measurable outcomes independently.
Product Mindset - Ability to scope MVPs, prioritise ruthlessly, and deliver iteratively. In addition, consider adoption, user experience, and business outcomes.

Nice To Haves

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

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.