Senior AI Engineer

GitLab GitLab · Enterprise · India · Enterprise Applications

Senior AI Engineer at GitLab responsible for delivering internal AI-powered solutions across Sales, Marketing, and Customer Support. The role involves diagnosing business problems, designing and shipping AI solutions end-to-end, improving organizational flow, and integrating AI capabilities into existing systems. Emphasis on practical outcomes, measurable business value, and leveraging AI as a productivity multiplier.

What you'd actually do

  1. 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.
  2. Own AI initiatives end-to-end, from stakeholder discovery and technical design through implementation, deployment, and iteration.
  3. 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.
  4. Improve organizational flow by building solutions that reduce bottlenecks, shorten lead times, and increase throughput. Measure success using flow metrics alongside adoption and ROI.
  5. 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.

Skills

Required

  • Strong proficiency in at least one modern scripting language (Python, JavaScript/TypeScript, or similar)
  • Solid understanding of REST APIs, GraphQL, and integration patterns
  • Deep, practical experience with modern AI technologies
  • 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.

Nice to have

  • Competent, Confident Coding Skills - You can build working solutions end-to-end, write clean and maintainable code, and debug effectively.

What the JD emphasized

  • AI-powered solutions
  • AI-first company
  • AI solutions
  • AI is the right intervention
  • AI-powered solutions
  • AI capabilities
  • AI is a powerful part of your toolkit
  • AI & LLM Technical Depth
  • Agentic architecture patterns

Other signals

  • deliver internal AI-powered solutions
  • AI-first company
  • AI solutions into key systems and workflows
  • build working solutions end-to-end
  • practical outcomes and measurable business value
  • integrate AI capabilities into existing systems and workflows