GitLab
Mid
AI Engineer, Enterprise Technology & AI at GitLab — diagnose before you build, then ship AI solutions into Sales, Marketing, and Support
GitLab's Enterprise Technology & AI team hires an AI Engineer to embed AI-powered solutions into internal Sales, Marketing, and Customer Support workflows, reporting to the Director of Enterprise AI. The role is explicitly discovery-first: map workflows, find the real constraint, and be willing to say AI isn't the answer before writing any code. This card focuses on the diagnose-then-build mindset, agentic architecture and prompt-engineering depth, AI safety guardrails, and fluency across enterprise systems like Salesforce, Zendesk, Workato, and Glean.
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What this interview tests
- Workflow diagnosis before building (flow metrics, constraint identification)
- Prompt engineering and context-window management
- Model selection and RAG vs. context tradeoffs
- Agentic architecture: tool use, multi-agent orchestration, guardrails
- AI safety and risk mitigation (prompt injection, data leakage)
- Integrating AI into enterprise systems (Salesforce, Zendesk, Workato)
Common question themes
Tell me about a time you decided AI was NOT the right solution to a business problem
How would you map a cross-team workflow to find the real bottleneck before proposing a fix
Walk through designing a multi-agent system with guardrails for an internal support workflow
When would you choose a smaller fine-tuned model over a general-purpose LLM, and why
How do you defend an AI-powered internal tool against prompt injection or data leakage
Describe shipping a working AI prototype in days — what did you cut to move fast
How do you measure the success of an AI initiative beyond adoption numbers
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