AI Engineer

You will diagnose business problems, map workflows, identify constraints, and determine whether AI is the appropriate solution. You will own AI initiatives from stakeholder discovery and technical design through implementation, deployment, measurement, and iteration. You will build and integrate practical AI solutions into business systems and workflows, using APIs, orchestration tools, modern AI platforms, custom code, or prompts as appropriate. You will collaborate with cross-functional stakeholders, track business and flow metrics, evaluate tools, document reusable patterns, and provide real-world feedback on AI offerings.

Responsibilities

• Diagnose business problems by mapping workflows, identifying constraints, and validating whether AI is the right intervention.
• Own AI initiatives from stakeholder discovery and technical design through implementation, deployment, and iteration.
• Design, develop, and ship AI-powered solutions focused on measurable business value.
• Build solutions that reduce bottlenecks, shorten lead times, and increase throughput.
• Integrate AI capabilities into existing systems and workflows using APIs, orchestration tools, and AI platforms.
• Use and showcase GitLab AI offerings and feed real-world usage insights back to R&D.
• Partner with cross-functional stakeholders to understand constraints and align on outcomes.
• Define and track business metrics, flow metrics, adoption, ROI, and feedback loops.
• Evaluate tools, document patterns, and create reusable technical foundations.

Requirements

• Demonstrated ability to build, maintain, and debug production-quality solutions independently.
• Proficiency in a modern scripting language such as Python or JavaScript/TypeScript.
• Understanding of REST APIs, GraphQL, and integration patterns.
• Practical experience with prompt engineering, context management, multi-turn interactions, output evaluation, and prompt iteration.
• Understanding of model selection, cost-performance trade-offs, fine-tuned models, RAG, and context-window design.
• Experience with agentic architecture patterns, including tool use, multi-agent orchestration, human-in-the-loop designs, guardrails, evaluation frameworks, and reliability patterns.
• Hands-on experience with modern AI technologies and LLMs from Anthropic, OpenAI, and open-source alternatives.
• Knowledge of AI safety practices, including input validation, output filtering, access controls, prompt-injection defenses, and data-leakage prevention.
• Ability to map end-to-end processes, identify bottlenecks, and trace root causes.
• Familiarity with enterprise business systems, data models, and workflows, including CRM, marketing automation, support, integration, AI, search, and knowledge tools.
• Ability to understand diverse functional needs and communicate with technical and non-technical stakeholders.
• Track record of owning complex initiatives from discovery through delivery.
• Ability to scope MVPs, prioritize, deliver iteratively, and consider adoption, user experience, and business outcomes.

Benefits

• Health, financial, and well-being benefits
• Flexible paid time off
• Team Member Resource Groups
• Equity compensation
• Employee Stock Purchase Plan
• Growth and Development Fund
• Parental leave