Machine Learning Operations - MLOps

About the job Job Title: Machine Learning Operations - ML Ops Job Location: Madrid, Spain Job Type : Permanent Responsibilities: Expand the scope of Advanced Analytics and AI, enabling cases that leverage the latest emerging technologies, 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, also collaborating with data engineers and data analysts. Deploy and optimize the AI models that leverage large-scale data to deliver predictive and analytical capabilities for the AST&I domains, delivering scalable and efficient solutions. Build and maintain end-to-end ML pipelines, ensuring model reproducibility, scalability, and monitoring in alignment with best practices for MLOps (feature engineering, model training, evaluation, and deployment in production environments). Engage in complex long-term projects, with focus on continuous delivery in small increments, with possibility of effectively leading and planning the projects to ensure successful outcomes. Engage and guide non-technical stakeholders and team members on Advanced Analytics and GenAI cases. Collaborate with multidisciplinary teams and manage different stakeholders. You will be part of diverse Agile/Scrum DevOps team and have end-to-end responsibility, for developing, managing, and maintaining functionalities in the AST&I area, which are prioritized by the Product Owner. Stay curious and up to date with trends of technologies, advanced analytics, genAI and cloud platforms (Databricks). Skills Required: Experience in machine learning engineering or applied ML with a focus on Azure cloud technologies. Proficient in Python (preferably OOP), PySpark and strong experience with ML frameworks such as Scikit-learn, TensorFlow, PyTorch. Hands-on experience with Databricks platform tools. Solid understanding of data preprocessing, feature engineering, and model optimization. Experience with ML pipeline orchestration using tools like MLflow, Azure Machine Learning. Excellent understanding of evaluation metrics and ML evaluation methods such as A/B testing and cross-validation. Experience with Git and building CI/CD pipelines for ML models, preferably in Azure DevOps/Azure Pipelines.