pul liBuild 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 /li liOwn the full lifecycle of every solution: from design and implementation through production deployment, monitoring, and continuous improvement based on user feedback /li liCollaborate closely with the AI Platform team to leverage shared infrastructure, align on technical architecture, and get guidance on the AI stack /li liCommunicate progress and findings clearly to both technical peers and non-technical stakeholders; present results to chapter leads and business owners /li liProduce lightweight documentation and run enablement sessions so business teams can work confidently alongside the solutions you create /li liBuild 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 /li liMonth 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 /li liMonth 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 /li liMonth 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 /li /ul h3Benefits /h3 ul liGlobal company and culture /li liFast growth path /li liHybrid working model /li /ul pAI 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 /p pWe’re looking for someone at the intersection of AI engineering and business understanding, who can turn a department’s problems into working AI solutions /p pCommunication 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 /p pEngineering fundamentals: Working knowledge of cloud platforms (AWS, GCP, or Azure), REST APIs (FastAPI or similar), and SQL for data access and integration /p pBusiness 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 /p pStartup mindset ownership: Comfortable with ambiguity, proactive, and accountable. You have the ability to take a vision and bring it to life /p /p #J-18808-Ljbffr

📌 Applied AI Engineer & AI Enablement (Madrid)
🏢 Aily Labs
📍 Madrid