Machine Learning Engineer - Hybrid Remote

Machine Learning Engineer - Hybrid Remote Avanade Madrid, area metropolitana (comarca); comunidad de madrid, Spain

Descripción del trabajo Machine Learning Engineer Madrid, Spain Compruebe que cumple con los requisitos de habilidades para este puesto, así como con la experiencia asociada. We have two parent companies that give us a strong Microsoft ecosystem with space to be ourselves. You will have the opportunity to utilize the most advanced technology within the Microsoft ecosystem, collaborating with some of the world's largest and most renowned companies, as well as working alongside highly intelligent individuals. Thanks to Microsoft and Avanade's strategic investments in AI and OpenAI, we are uniquely positioned to help our clients become AI-first organizations. By joining the Hub, you will be part of a delivery pod working in an agile setup, owning AI initiatives from problem framing and data exploration to model development, deployment, and adoption.

As a Senior Analyst - AI & Data Science, you will design, develop, and deliver AI-and data-driven solutions that help our clients achieve measurable business outcomes. This role combines strong Data Science foundations with hands‑on AI engineering, including recent GenAI use cases. You will work across the full data science lifecycle: data exploration, feature engineering, model development, evaluation, and deployment, while also contributing to modern AI solutions such as LLM-based applications, NLP, computer vision, and predictive analytics, primarily on Microsoft Azure. Design and deliver end-to-end Data Science and AI solutions, from business understanding and data exploration to model deployment and monitoring. Perform exploratory data analysis (EDA), feature engineering, and data preprocessing on structured and unstructured datasets.

Develop, train, evaluate, and optimize machine learning and deep learning models, selecting appropriate algorithms and validation strategies. Contribute to Generative AI solutions, including LLM-based applications, prompt engineering, RAG architectures, and applied NLP use cases. Stay up to date with the latest advancements in Data Science, ML, DL, and GenAI, and actively share knowledge within the team. Contribute to reusable assets such as code templates, analytical frameworks, and internal training materials. Collaborate with senior team members and architects to identify opportunities where advanced analytics and AI can transform client operations.