Machine Learning Engineer (AI)

TL;DR

Machine Learning Engineer (AI): Designing, building, and maintaining robust, scalable AI solutions within a DevOps environment with an accent on LLM orchestration, RAG systems, and agentic workflows. Focus on developing multi-agent systems, optimizing AI model performance, and ensuring reliable, consistent AI outputs for wealth management applications.

Location: Madrid

Company

hirify.global is a leading wealth management group dedicated to creating value beyond wealth through innovation and entrepreneurial spirit.

What you will do

• Develop, deploy, and optimize machine learning and AI solutions to address complex business challenges.
• Create multi-agent systems and implement RAG systems to ground AI responses in enterprise data.
• Assess and integrate LLMs to ensure optimal performance and reliability in production.
• Optimize agentic workflows and perform rigorous evaluation of AI models using metrics and evaluation sets.
• Collaborate with platform teams, data engineers, and data scientists to integrate AI solutions into existing systems.
• Participate in code reviews, testing, and debugging to maintain high-quality, reliable code.

Requirements

• English: C1 proficiency required
• Bachelor's or Master's degree in Data Science, Computer Science, Mathematics, or a related field.
• Strong programming skills in Python with experience in TensorFlow or PyTorch.
• Proven experience working with Large Language Models (LLMs) and orchestration frameworks.
• Solid understanding of AI agents, agentic workflows, and reasoning patterns.
• Experience with data preprocessing, feature engineering, and model evaluation techniques.

Nice to have

• Spanish language proficiency is a plus.

Culture & Benefits

• Opportunity to work in an agile development team within a major wealth management institution.
• Focus on continuous learning and staying up-to-date with AI advancements.
• Collaborative environment working with cross-functional teams.
• Commitment to high-quality, maintainable software development practices.