Python Machine Learning Engineer (Hybrid - Madrid)

Role overview

Responsibilities

• check_circle Develop, deploy, and optimize Machine Learning and AI solutions for complex business challenges.
• check_circle Design and implement multi-agent systems and enable AI models with function/tool-calling capabilities.
• check_circle Build and maintain Retrieval-Augmented Generation (RAG) solutions using enterprise data.
• check_circle Evaluate, integrate, and optimize AI models, including Large Language Models (LLMs).
• check_circle Design and improve agentic workflows and AI agents for production environments.
• check_circle Test and optimize prompts and few-shot examples to ensure accurate, consistent, and safe AI outputs.
• check_circle Evaluate model performance using appropriate metrics, benchmarks, and validation techniques.
• check_circle Collaborate with Data Scientists, Data Engineers, Platform Teams, and other stakeholders.
• check_circle Participate in code reviews, testing, debugging, and quality assurance activities.
• check_circle Contribute to the full AI lifecycle, from design and development to deployment and monitoring.

Basic qualifications

• Bachelor’s or Master’s degree in Computer Science, Data Science, Mathematics, Statistics, or a related field.
• Strong programming skills in Python.
• Hands-on experience with Machine Learning frameworks such as TensorFlow and/or PyTorch.
• Proven experience working with Large Language Models (LLMs).
• Solid understanding of AI Agents, agentic workflows, orchestration frameworks, and reasoning patterns.
• Experience designing and implementing RAG architectures.
• Knowledge of data preprocessing, feature engineering, model selection, and evaluation techniques.
• Strong understanding of statistics, probability, linear algebra, and optimization concepts.
• Experience applying software engineering best practices, including version control, testing, and documentation.
• English C1

Preferred qualifications

• Experience working in cloud environments (Azure, AWS, or GCP).
• Knowledge of MLOps and CI/CD practices.
• Experience deploying and monitoring AI solutions in production.
• Background in financial services, banking, or wealth management.
• Familiarity with vector databases and modern GenAI ecosystems.

Benefits

• check_circle 23 days of Annual Leave plus the 24th and 31st of December as discretionary days!
• check_circle Numerous benefits (Heath Care Plan, Internet Connectivity, Life and Accident Insurances).
• check_circle `Retribución Flexible´ Program: (Meals, Kinder Garden, Transport, online English lessons, Heath Care Plan…)
• check_circle Free access to several training platforms
• check_circle Professional stability and career plans
• check_circle UST also, compensates referrals from which you could benefit when you refer professionals.
• check_circle The option to pick between 12 or 14 payments along the year.
• check_circle Real Work Life Balance measures (flexibility, WFH or remote work policy, compacted hours during summertime…)
• check_circle UST Club Platform discounts and gym Access discounts

About the company