Are you an Azure Machine Learning Engineer seeking a new interesting challenge? If your answer is yes, it’s your lucky day so keep reading — it can be just what you're looking for! What will you do? Expand the scope of Advanced Analytics and AI, enabling use cases that leverage emerging technologies and fostering an innovative environment. Work closely with the Data Scientists of the Advanced Analytics & AI Center of Expertise to design and develop Machine Learning and Generative AI models, collaborating with data engineers and data analysts. Deploy and optimize AI models that leverage large-scale data to deliver predictive and analytical capabilities for the AST&I domains. Build and maintain end-to-end ML pipelines, ensuring model reproducibility, scalability and monitoring aligned with MLOps best practices. Engage in complex long-term projects, focusing on continuous delivery in small increments and contributing to project planning and execution. Engage and guide non-technical stakeholders on Advanced Analytics and Generative AI use cases. Collaborate with multidisciplinary teams and manage multiple stakeholders. Be part of a diverse Agile / Scrum DevOps team with end-to-end responsibility for developing, managing and maintaining functionalities in the AST&I area, prioritized by the Product Owner. Stay curious and up to date with trends in advanced analytics, generative AI and cloud platforms such as Databricks. What technologies will you use? Python (OOP preferred) PySpark Machine Learning frameworks: Scikit-learn, TensorFlow, PyTorch Databricks platform tools Azure Machine Learning, MLflow and ML pipeline orchestration tools Git and CI/CD pipelines for ML models (Azure DevOps / Azure Pipelines) MLOps practices (feature engineering, training, evaluation and deployment) Agile / Scrum methodologies #J-18808-Ljbffr