AI Lead

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 global manufacturing enterprise
• Understanding of data privacy, AI regulation (EU AI Act), and compliance requirements
• Business value framing: translating AI capabilities into operational impact

Behavioral & Leadership

• Innovation mindset balanced with pragmatic delivery
• Ability to translate AI concepts for non-technical business audiences
• Responsible AI advocacy - champions governance alongside capability
• Hypothesis-driven experimentation: tests before scaling
• Strong cross-domain collaboration with data engineering, analytics, and business units

Let op: vacaturefraude

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Important: job fraud

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