Jesús Ruiz is a Head of Artificial Intelligence based in Madrid with eight years of experience building production-grade ML and big data systems that bridge research and business impact. He has led end-to-end programs from feature-engineered transformer backbones for multi-hub logistics forecasting to LLM-driven modernization of COBOL core banking, delivering measurable gains like <10% MAPE across 30+ centers and 2× BLEU versus GPT-4 on code translation. Comfortable both coding and coordinating, he designs scalable data architectures (Delta Live Tables, Databricks, MLflow, MLOps) and implements deployment strategies from GPU pooling to serverless endpoints. His background in teaching and executive education informs a pragmatic approach to governance, explainability and team enablement, while his academic training spans Stanford, MIT and ongoing LLMOps study at Duke. An active contributor to open-source front-end projects such as omegaUp, he combines UX-aware engineering with deep NLP and retrieval work (RAG, embeddings, FAISS) used in commercial agents and knowledge graphs. Notably, he pairs hands-on model fine-tuning and pruning at scale with operational practices (drift detection, config-as-code) that make advanced AI reliably usable in regulated industries.
8 years of coding experience
2 years of employment as a software developer
Master in Civil Engineering, Master in Civil Engineering at Universidad Politécnica de Madrid
Programa especializado: Large Language Models Operations (LLMOps) (on-going), Programa especializado: Large Language Models Operations (LLMOps) (on-going) at Duke University
Remote Deep Learning Program, Artificial Intelligence, Remote Deep Learning Program, Artificial Intelligence at Massachusetts Institute of Technology
Machine Learning Course, Artificial Intelligence, Machine Learning Course, Artificial Intelligence at Stanford University
Contributions:54 commits, 58 PRs, 325 comments in 7 months
Contributions summary:Jesús primarily contributed to the frontend of the omegaUp platform, focusing on localization and UI improvements. Their work included modifying language files for several languages, updating badge descriptions and names, and migrating parts of the problem settings form to Vue.js and Bootstrap 4. They also addressed UI issues, such as the display of problem statuses, and implemented functionality for searching nominations. These contributions demonstrate a focus on enhancing user experience and internationalization within the platform.
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