June 2026 archive · For research and skill exploration. Current job availability is unverified.
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GitLab

AI Engineer

Build internal AI solutions driving measurable business outcomes across sales, marketing, and support operations.

GitLabText summary from the June 2026 archive. original source →

The role

As an AI Engineer at GitLab, you'll lead the technical delivery of internal AI-powered solutions that transform how the organization operates. Reporting to the Director of Enterprise AI, you'll combine hands-on engineering with systems thinking to diagnose real business problems, validate whether AI is the right solution, and own initiatives from discovery through deployment and iteration.

What you'd do

  • Diagnose organizational bottlenecks and constraints before proposing AI interventions
  • Own end-to-end AI initiatives from stakeholder discovery through technical design, implementation, and deployment
  • Design and deliver working AI prototypes rapidly, prioritizing practical business outcomes over perfect solutions
  • Integrate AI capabilities into existing enterprise systems using APIs, orchestration tools, and modern platforms
  • Serve as Customer Zero by leveraging GitLab's AI offerings and feeding real-world insights back to product teams
  • Partner across functions to understand constraints and align stakeholders on measurable success metrics
  • Define and track success using business metrics, flow metrics, and actionable feedback loops
  • Establish technical direction by evaluating tools, documenting patterns, and building reusable foundations

What they're looking for

  • Strong coding skills with ability to build production-quality solutions independently
  • Proficiency in at least one modern scripting language such as Python or JavaScript/TypeScript
  • Solid understanding of REST APIs, GraphQL, and integration patterns
  • Deep practical experience with prompt engineering, including system design and output evaluation
  • Knowledge of model selection trade-offs and when to use RAG versus context expansion
  • Hands-on experience with agentic architecture patterns and multi-agent orchestration
  • Practical familiarity across the LLM ecosystem including Anthropic, OpenAI, and open-source models
  • Critical thinking about AI safety, guardrails, prompt injection defense, and data leakage prevention
  • Systems thinking ability to map workflows, identify bottlenecks, and diagnose root causes
  • Familiarity with enterprise business systems including Salesforce, Marketo, Zendesk, and similar platforms
  • Understanding of enterprise data models and workflows
  • Track record of owning complex initiatives from discovery through delivery
  • Ability to operate with ambiguity and drive toward measurable outcomes independently
  • Product mindset with ability to scope MVPs, prioritize, and deliver iteratively

Nice to have

  • 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
  • Experience with low-code/no-code orchestration tools such as n8n, Make, or Workato
  • Previous startup or high-growth company experience
  • Experience mentoring or leading technical projects with junior engineers

Summary written by RoleDeck from the original posting. This is an extracted, own-words summary and may contain errors. The original source may have changed or expired.