- Build and deploy a wide range of AI solutions end-to-end: forecasting algorithms, classification and regression models, LLM-powered tools, AI agents, or any technique best suited to the problem at hand.
- Own the full lifecycle of every solution: from design and implementation through production deployment, monitoring, and continuous improvement based on user feedback.
- Collaborate closely with the AI Platform team to leverage shared infrastructure, align on technical architecture, and get guidance on the AI stack.
- Communicate progress and findings clearly to both technical peers and non-technical stakeholders; present results to chapter leads and business owners.
- Produce lightweight documentation and run enablement sessions so business teams can work confidently alongside the solutions you create.
- Build from the ground up: Join us at the beginning of this journey, working closely with the team Lead to shape the strategy, culture, and technical foundations of our new team.
Applied AI Engineer & AI Enablement
Barcelona - Hybrid, Madrid - Hybrid
Full-time
Permanent employee
Mission
Your profile
Journey to impact:
- Month 1: Get up to speed with 1–2 business units. Understand their data, workflows, and business context. Pick up the first strategic brief from the AI Strategist Principal, and deliver a working AI prototype that solves a real daily pain point.
- Month 3: Have at least one solution running in production. Incorporate feedback from real users, stabilise monitoring, and document the solution. Begin a second chapter engagement and broaden the range of AI techniques you apply.
- Month 6: Become a trusted enabler within the company and the Strategy team. Your solutions are running reliably and reusable components are being picked up by other teams.
- AI & technical foundation: Strong Python skills and practical experience across the AI/ML spectrum: forecasting, classification, regression, LLMs, and agentic frameworks (LangChain, LlamaIndex, CrewAI, or equivalent). Comfortable owning solutions in production. Familiarity with Docker/K8s or MLOps tooling.
- Engineering fundamentals: Working knowledge of cloud platforms (AWS, GCP, or Azure), REST APIs (FastAPI or similar), and SQL for data access and integration.
- Business & requirements acumen: Able to sit with a non-technical team, ask the right questions, identify what actually matters, and translate messy real-world context into a clear implementation plan.
- Communication & collaboration: Clear and confident communicator with both technical and non-technical audiences. You can explain what a model does, and what it can't do, to someone with no AI background.
- Startup mindset & ownership: Comfortable with ambiguity, proactive, and accountable. You have the ability to take a vision and bring it to life.