Senior Machine Learning Engineer

Were looking for a Senior ML Engineer to join our team in Madrid Spain in a hybrid working this role you will design build and deploy scalable machine learning and AI solutions that power next-generation digital capabilities within a leading global financial institution. You will work across the full lifecycle from concept and prototyping to production in an agile and DevOps-oriented environment collaborating with multi-disciplinary teams to deliver robust business-critical AI systems.

If you are passionate about Large Language Models multi-agent workflows and advanced ML engineering practices this is an opportunity to shape AI-driven innovation within one of the worlds most renowned wealth management organizations.

• Design develop deploy and optimize machine learning and AI solutions addressing complex business challenges
• Build and integrate multi-agent systems and enable AI models with function/tool calling capabilities
• Design and maintain RAG (Retrieval-Augmented Generation) systems to ground AI outputs in enterprise data
• Integrate and fine-tune Large Language Models (LLMs) to ensure performance consistency and reliability
• Optimize agentic workflows for production use cases while ensuring safety and accuracy
• Evaluate and improve system performance using robust metrics evaluation sets and continuous iteration
• Collaborate with data engineers platform teams and data scientists to integrate ML solutions into enterprise systems
• Conduct code reviews unit testing and debugging to guarantee quality and maintainability
• Ensure compliance with software development best practices across version control testing and documentation

• Bachelors or Masters degree in Data Science Computer Science Mathematics Statistics or related field
• Proven experience as a Machine Learning Engineer or similar role in AI solution development
• Strong programming skills in Python experience with ML libraries and deep learning frameworks (TensorFlow or PyTorch)
• Practical experience implementing and deploying LLMs and related orchestration frameworks
• Knowledge of agentic workflows multi-agent systems and advanced reasoning patterns
• Strong understanding of data preprocessing feature engineering and model evaluation techniques
• Deep familiarity with relevant mathematical and statistical concepts (probability linear algebra optimization)
• Experience implementing MLOps practices and working in DevOps-based environments
• Excellent problem-solving debugging and optimization skills
• Strong communication and ability to collaborate with cross-functional teams in an agile environment

• Experience designing RAG systems for enterprise-scale use
• Prior exposure to AI governance security or compliance in financial services
• Familiarity with cloud infrastructures and containerized ML deployments using Kubernetes
• Proven track record of enabling AI-driven applications in production environments