Omya
Linkedin · Posted 3d ago
AI Lead
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Indexed description
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
- Machine learning and advanced analytics concepts
- Analytics-ready and AI-ready datasets
- Model validation, monitoring, and performance tracking
- Cloud-based analytics and AI platforms
- 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
- 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.
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