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: Lang. Chain, 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 / Deep. Learning.AI)

• Strongly Preferred Microsoft Certified: Fabric Analytics Engineer Associate — Preferred

1. 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
2. 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 What do we offer? Hybrid Work Model : Flexibility to work from home and in the office, according to the policy, helping you achieve a healthy work-life balance. Ticket Restaurant : Enjoy a daily meal allowance to support your well-being. Flexible retribution : Kindergarten & Transport 30 Labor Days of Holidays : Ample time off to relax and recharge. Language Lessons : Access to language lessons to help you grow both personally and professionally. Medical Insurance : 60% company-subsidized medical insurance for employees, with the option to extend coverage to family members at a highly competitive rate. Open and Modern Office Environment : Work in a collaborative, innovative, and comfortable space designed for your success.