We are looking for a Principal AI Engineer to join our Enterprise Productivity team, an agile, high-impact group building AI-driven solutions that transform how our company operates. In this role, you will work at the intersection of AI, engineering, and business operations, partnering directly with teams across the company to identify inefficiencies, rapidly prototype solutions, and deploy scalable AI tools that drive measurable impact. You will operate globally, engage with diverse stakeholders, and tackle high-leverage problems that improve how we work every day. This is a fast-moving, hands-on role for someone who thrives in ambiguity, enjoys solving real-world problems, and wants to see their work adopted quickly at scale.Job DescriptionWhat You’ll DoEmbed with business teams to deeply understand workflows, pain points, and opportunities for automation and optimizationDesign and build AI-powered tools that improve internal productivity across functions such as finance, HR, operations, sales, and marketingRapidly prototype and iterate using modern AI/ML technologies (e.G., LLMs,



Copilots, automation pipelines)Deploy solutions in production, ensuring reliability, usability, and measurable impactBridge technical and non-technical teams, translating business problems into scalable engineering solutionsDrive end-to-end delivery from problem discovery to implementation, rollout, and adoptionCreate reusable frameworks and platforms to accelerate future AI deployments across the organizationOperate globally, collaborating with distributed teams and delivering solutions that scale across regionsQualificationsCore Skills7–10+ years of experience in software engineering, machine learning, or applied AIStrong experience designing and deploying production-grade systems in cloud environmentsExperience building and deploying AI/ML systems, ideally with LLMs or generative AIExperience in building, orchestrating, monitoring agentic/multi-agentic AI workflowsAbility to work acro

📌 Ai Principal Engineer (Madrid)
🏢 Niq
📍 Madrid