Job Main Responsibilities
- Support development and deployment of AI and advanced analytics use cases
- Prepare, validate, and curate data for AI workloads
- Ensure quality, performance, and governance of AI outputs
- Collaborate with data engineering and analytics teams
- Support responsible and scalable AI adoption

Technical Skills & Technology Landscape
- Machine learning and advanced analytics concepts
- Analytics-ready and AI-ready datasets
- Model validation, monitoring, and performance tracking
- Cloud-based analytics and AI platforms

Qualifications And Skills
- Core Technical
- LLM orchestration frameworks: LangChain, Semantic Kernel, Azure AI Foundry
- MLOps practices: model versioning, deployment pipelines, monitoring (MLflow, Azure ML)
- Prompt engineering: few-shot, chain-of-thought, structured output, retrieval-augmented generation (RAG)Azure AI Services: Azure OpenAI, Cognitive Services, AI Search (vector and hybrid)
- Feature engineering and ML pipeline development (Databricks Feature Store, MLflow)
- Responsible AI: bias detection, explainability,



AI governance frameworks
- AI-ready data design: embedding generation, vector store management, data curation for AI
- API integration: exposing AI capabilities as enterprise services (FastAPI, Azure API Management)
- Certifications
- Microsoft Certified: Azure AI Engineer Associate — Preferred
- Microsoft Certified: Azure AI Fundamentals — Preferred
- Databricks Certified Machine Learning Professional — Preferred
- Generative AI for Business Leaders (Microsoft / Coursera / DeepLearning.AI) - Strongly Preferred
- Microsoft Certified: Fabric Analytics Engineer Associate — Preferred
- Industry & Business Knowledge
- Industrial AI use cases: predictive maintenance, quality control, demand sensing
- SAP data context for AI inputs: finance forecasting, procurement analytics, production data
- Responsible AI governance in a general manufacturing enterprise
- Understanding of data privacy, AI regulation (EU AI Ac

📌 AI Lead (España)
🏢 Omya
📍 España