AI Engineer

Details

As an 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.

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

• Technologist at Heart: Genuinely invested in technology; strong engineering fundamentals; reach for the simplest solution that solves the problem well.
• Competent, Confident Coding Skills: Build working solutions end-to-end, write clean and maintainable code, debug effectively, and deliver production-quality work independently.
• AI & LLM Technical Depth:
  • Strong proficiency in at least one modern scripting language (Python, JavaScript/TypeScript, or similar).
  • Solid understanding of REST APIs, GraphQL, and integration patterns.
  • 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: knowing when smaller fine-tuned models outperform larger general-purpose models; understanding when RAG is the right architecture vs expanding context; making decisions about capability vs 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, including models from Anthropic, OpenAI, open-source alternatives.
• AI Safety & Risk Awareness: Design guardrails including input validation, output filtering, access controls, prompt injection defenses, and data leakage prevention.
• Systems Thinking & Diagnostic Rigour: Map end-to-end work flows, identify bottlenecks, trace problems to root causes, and understand constraints.
• Business System Expertise: Familiarity with enterprise business systems such as CRM (Salesforce), marketing automation (Marketo), support platforms (Zendesk), integration/orchestration (Workato), AI platforms (Relevance AI), and enterprise search/knowledge tools (Glean). Understand enterprise data models and workflows.
• Broad Functional Understanding: Ability to have meaningful conversations with stakeholders across diverse domains.
• End-to-End Ownership: Track record owning complex initiatives from discovery through delivery; comfortable operating with ambiguity and driving measurable outcomes independently.
• Product Mindset: Scope MVPs, prioritize, deliver iteratively, and consider adoption, user experience, and business outcomes.

Benefits

• 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