ML Engineer - AI Agents, RAG & Production

Bank Julius Bär & Co. Ltd.; Comunidad de Madrid; On-site; EUR 65,000 - 100,000; Full time

Job summary

Bank Julius Bär & Co. Ltd. is seeking a skilled machine learning engineer to join an agile ML/AI team in the ML & AI ART domain.

You will design, build, deploy, and maintain robust AI solutions within a DevOps environment, collaborating with data engineers, data scientists, and platform teams across the full lifecycle from concept to deployment. The role emphasizes developing multi-agent systems, grounding AI outputs with enterprise data, and optimizing agentic workflows for production use

Qualifications

• Bachelor’s or Master’s degree in Data Science, Computer Science, Mathematics, Statistics, or a related field.
• Strong programming skills in Python and ML libraries.
• Proven experience with Large Language Models (LLMs).
• Experience with data preprocessing, feature engineering, and model evaluation.
• Knowledge of AI agents, agentic workflows and LLM orchestration frameworks.
• Experience in DevOps environments and end-to-end ML lifecycle.

Responsibilities

• Develop, deploy, and optimize ML and AI solutions addressing complex business challenges.
• Create multi-agent systems with function/tool-calling capabilities.
• Design and implement RAG systems grounded in enterprise data.
• Assess and integrate AI models ensuring performance and reliability.
• Collaborate with platform teams, data engineers, and data scientists to integrate solutions.
• Participate in code reviews, testing, and debugging to ensure quality and reliability.

Skills

Problem-solving

Analytical thinking

Python programming

LLMs experience

TensorFlow/PyTorch

DevOps mindset

Cross-functional collaboration

Education

Bachelor's or Master's in Data Science/CS/Math/Stats

Tools

TensorFlow

PyTorch

Job description

Bank Julius Bär & Co. Ltd. is seeking a skilled machine learning engineer to join an agile ML/AI team in the ML & AI ART domain.

You will design, build, deploy, and maintain robust AI solutions within a DevOps environment, collaborating with data engineers, data scientists, and platform teams across the full lifecycle from concept to deployment. The role emphasizes developing multi-agent systems, grounding AI outputs with enterprise data, and optimizing agentic workflows for production use