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 Act), and compliance requireme

📌 AI Lead (Madrid)
🏢 Omya
📍 Madrid