At RHI Magnesita, we are transforming how data and AI drive our global operations. As a Data Scientist, you will design, build, and deploy end-to-end AI and ML solutions that impact critical business processes across our industrial value chain. Joining our central Data & Analytics team, you will collaborate with data engineers, product teams, and domain experts to deliver high‑impact AI and ML systems while ensuring data quality, lineage, and governance.This is a chance to shape the future of AI at the world’s leading refractory company by building models, platforms, and infrastructure that matter.

• Languages & frameworks: Python, SQL, PyTorch/TensorFlow, Pandas, Spark
• Platforms: Databricks, MLflow, Vector Databases, Lakehouse/Delta
• DevOps & workflows: Azure DevOps, Docker, CI/CD
• Cloud: Azure

• Designing, developing, and maintaining end‑to‑end ML pipelines for training, evaluation, and deployment across batch and real‑time use cases
• Productionizing ML models via APIs or batch inference, including telemetry, A/B testing, drift detection, and automated monitoring
• Building reliable data preparation and feature engineering components
• Optimizing model training and inference performance and cost, including hardware selection, caching, vectorization, quantization, and scalable endpoints
• Establishing and maintaining CI/CD workflows for ML systems, contributing to platform standards, documentation, and runbooks

• Bachelor’s or Master’s degree in Data Science, Computer Science, Engineering, Mathematics or a related field of study
• Several years of experience in building production-grade ML systems, with Python and ML frameworks (pandas, numpy, scikit-learn, PyTorch, TensorFlow) as well as containerization and CI/CD (Git, Docker, orchestration/workflows)
• Proficiency in SQL and data modeling with EDA for pattern and dependencies identification.
• Experience with Vector Indexes, Embedding Models, LLM agent patterns, ingestion pipelines, and model serving frameworks (e.g., MLflow, Databricks).
• Experience with unified data/AI platforms like Databricks, including Unity Catalog, governance concepts, A/B testing, causal inference, and experimentation platforms

Nice to have: Any relevant cloud or data/AI certifications. Familiarity with writing and debugging LLM tools in OOP python format, with focus on Generative AI (prompting, RAG, vector search) and MLOps