Senior AI Engineer

Open 18d

You will diagnose business problems and map workflows before deciding whether AI is the right solution. You will own AI initiatives from stakeholder discovery and technical design through implementation, deployment, and iteration. You will rapidly build practical AI-powered solutions, integrate them into enterprise systems and workflows, measure adoption, ROI, and flow metrics, and help establish reusable technical patterns.

Responsibilities

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

Requirements

Demonstrate strong end-to-end coding, code maintenance, and debugging skills.
Be proficient in at least one modern scripting language, such as Python or JavaScript/TypeScript.
Understand REST APIs, GraphQL, and integration patterns.
Apply prompt engineering, including system prompt design, context-window management, multi-turn interactions, and output evaluation.
Make model-selection and cost-performance decisions, including choosing between fine-tuned models, large models, RAG, and expanded context windows.
Apply agentic architecture patterns, including tool use, multi-agent orchestration, human-in-the-loop designs, guardrails, evaluation frameworks, and reliability patterns.
Have practical experience with Anthropic, OpenAI, and open-source LLM alternatives.
Design AI guardrails, including input validation, output filtering, access controls, prompt-injection defenses, and data-leakage prevention.
Map complex processes, identify bottlenecks, and trace root causes.
Understand enterprise data models and workflows, including CRM, marketing automation, support, integration, AI, search, and knowledge systems.
Own complex initiatives from discovery through delivery and drive measurable outcomes.
Scope MVPs, prioritize work, and deliver iteratively with adoption, user experience, and business outcomes in mind.