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:

1. 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)

2. 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

3. 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

4. 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