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 general 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 (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

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

📌 Senior Machine Learning Engineer (Madrid)
🏢 Epam Systems
📍 Madrid