Interviewing at GitLab

Give us your one-minute pitch: why are you the right AI Engineer to help GitLab become an AI-first company?

AI Engineer

Walk us through the design trade-offs in FacturaHub’s AI invoicing agent — specifically, how did you balance LLM model cost, latency, and output reliability?

AI Engineer

Can you describe a case where prompt design directly impacted system adoption or business outcomes? Include specifics from your AI projects.

AI Engineer

Give an example of how you handled security, access controls, or privacy in an AI/ML system, citing a specific product.

AI Engineer

How would you approach integrating an LLM-based agent into an existing SaaS DevOps pipeline (e.g., via API, orchestrator, or custom plugin)?

AI Engineer

Design a system to evaluate and trace LLM outputs for a multi-brand, production SaaS app (like DGuard). What are the key components?

AI Engineer

Describe your experience with REST or GraphQL integrations in production — what pitfalls have you hit, and how did you handle versioning and compatibility?

AI Engineer

Describe a time you had to push back on using AI as the solution. What happened, and what was the outcome?

AI Engineer

Walk us through a project where you identified a workflow bottleneck and delivered an AI-powered solution end-to-end. What made the AI component impactful?

AI Engineer

Explain how you orchestrated prompt engineering and retrieval in FacturaHub’s WhatsApp agent. What was most challenging at scale?

AI Engineer

Have you ever implemented or evaluated agentic architectures with human-in-the-loop or tool use? What frameworks did you use and why?

AI Engineer

How do you integrate LLM-powered features with robust backend APIs, and what practices ensure reliability and observability in production?

AI Engineer