Senior Machine Learning Engineer

We're looking for a Senior ML Engineer to join our team in Madrid, Spain in a hybrid working mode. In 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 world’s most renowned wealth management organizations.

Responsibilities

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 Requirements Bachelor’s or Master’s 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 (Tensor.

Flow or Py. Torch) 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 Nice to have 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