Job Overview Data Science & MLOps Engineer to join the Advanced Analytics & AI team. Focus on designing, developing, and deploying scalable machine learning and Generative AI solutions within an Azure-based ecosystem. Responsibilities * Design, develop, and deploy machine learning and Generative AI models for advanced analytics use cases. * Build and maintain end-to-end ML pipelines, including data preprocessing, feature engineering, model training, evaluation, and deployment. * Collaborate closely with data scientists, data engineers, and analysts to deliver scalable AI solutions. * Implement and manage MLOps best practices, ensuring model reproducibility, monitoring, and lifecycle management. * Optimize models for performance and scalability in production environments. * Work with tools such as MLflow, Azure Machine Learning, and Azure DevOps for pipeline orchestration and CI/CD. * Drive innovation by expanding AI use cases using emerging technologies such as Generative AI. * Communicate complex analytical concepts to non-technical stakeholders and guide decision-making.



* Participate in Agile/Scrum teams, contributing to continuous delivery and iterative product development. * Stay updated on industry trends in AI, MLOps, cloud computing, and data platforms. Qualifications * 6–8 years of experience in Machine Learning Engineering or Applied ML with strong exposure to MLOps. * Advanced proficiency in Python (OOP) and PySpark. * Hands‑on experience with Scikit‑learn, TensorFlow, or PyTorch. * Strong experience with Azure Cloud and Databricks. * Expertise in building ML pipelines using MLflow, Azure ML, and CI/CD tools (Azure DevOps). * Strong knowledge of data preprocessing, feature engineering, model optimization, and evaluation techniques (cross‑validation, A/B testing). * Experience with Git and collaborative development practices. * Graduate level education. * English (C1) required. Nice to Have * Knowledge of Generative AI frameworks (e.g., LangChain). * Famil

📌 Data Science cum MLOps, Madrid
🏢 TheWhiteam
📍 Madrid