AI Engineer

The role

This hands-on role sits within the Enterprise Technology and AI group and reports to the director of Enterprise AI. The engineer first studies how work moves across teams and confirms where AI genuinely helps, then owns delivery from discovery through rollout. Early work targets sales, marketing, and customer support systems.

What you would do

• Analyze workflows and locate the true bottleneck before deciding whether AI is warranted, including concluding that some problems need no AI.
• Lead initiatives end to end, covering stakeholder discovery, technical design, build, launch, and ongoing refinement.
• Ship working prototypes within days, favoring small increments that deliver measurable value.
• Reduce friction in cross-team processes to shorten lead times and raise throughput.
• Track flow measures alongside adoption and return on investment to judge results.
• Connect AI capabilities to existing platforms through APIs, orchestration services, and current AI products, choosing whichever tool fits best.
• Use GitLab's own AI offerings internally and relay real usage findings back to product teams.
• Clarify constraints with stakeholders in other functions and agree on outcomes before building.
• Evaluate candidate tools and document reusable patterns that others can adopt.

What they asked for

• Genuine interest in technology across foundational and emerging areas, preferring simple solutions over unnecessary novelty.
• Solid, production-ready coding ability in Python, JavaScript/TypeScript, or similar, with working knowledge of REST, GraphQL, and integration design.
• Applied experience with prompt design, context management, output evaluation, and model selection, including small tuned models versus large general ones and RAG versus longer context windows.
• Understanding of agent architectures such as tool calling, multi-agent coordination, human review checkpoints, guardrails, and reliability practices.
• Working familiarity with several LLM providers, including Anthropic, OpenAI, and open-source options.
• Awareness of AI misuse risks and ability to build safeguards such as input checks, output filtering, access controls, injection defenses, and data leakage prevention.
• Systems thinking to map processes and trace problems to root causes.
• Understanding of enterprise systems such as Salesforce, Marketo, Zendesk, Workato, Relevance AI, and Glean, and how their data flows together.
• Track record of owning complex initiatives independently in ambiguous settings.
• Product sense for scoping MVPs, prioritizing ruthlessly, and iterating based on adoption and business outcomes.

Nice to have

• Hands-on familiarity with GitLab and pipeline automation.
• Past work in client-facing technical positions such as advisory or pre-sales engineering.
• Knowledge of lean process analysis, throughput measurement, or constraint-focused problem solving.
• Skill with visual automation builders like n8n, Make, or Workato, paired with hand-written code.
• Prior work at fast-scaling or young companies.
• Track record of coaching less experienced engineers or steering technical initiatives.