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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.
- 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
- 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 (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