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
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
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.
Skills Required
- 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, 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 as enterprise services (FastAPI, Azure API Management)
- Microsoft Certified: Azure AI Engineer Associate
- Microsoft Certified: Azure AI Fundamentals
- Databricks Certified Machine Learning Professional
- Generative AI for Business Leaders (Microsoft / Coursera / DeepLearning.AI)
- Microsoft Certified: Fabric Analytics Engineer Associate
- Industrial AI use cases knowledge: 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
- Innovation mindset balanced with pragmatic delivery
- Ability to translate AI concepts for non-technical business audiences
- Responsible AI advocacy and championing governance alongside capability
- Hypothesis-driven experimentation: tests before scaling
- Strong cross-domain collaboration with data engineering, analytics, and business units
What We Do
Omya is a leading global producer of industrial minerals, primarily fillers and pigments derived from calcium carbonate and dolomite, and a worldwide distributor of specialty materials. The company operates across diverse sectors, including construction, polymers, pulp and paper, agriculture, and life sciences. It provides high-purity minerals and specialty ingredients designed to improve process efficiency and performance for customers in over 50 countries.
