Miguel Magaña-fuentes is a multidisciplinary AI and data science leader based in Zaragoza with over a decade of experience building production-grade ML systems for education and finance, including agents, knowledge graphs, and RAG pipelines. He blends deep research in quantum chemistry and non-Hermitian quantum mechanics with pragmatic engineering—having applied quantum computing to improve risk model calibration and lifted RAG quality through knowledge-graph methods. His work in credit risk, fraud, and KYC/AML delivered measurable business impact (e.g., reduced defaults and optimized model performance via Bayesian methods), while his engineering focus includes deployment, feature engineering at scale, and rigorous testing. As an educator and curriculum designer he has led bootcamps and taught hundreds of students, translating complex theory into hands-on learning experiences. Comfortable leading cross-functional teams and advising startups, he moves ideas from prototype to production and is equally at home in research code or cloud deployment pipelines. An uncommon combination of lab-bench research, quantum computing training, and production ML gives him a unique edge in tackling high-impact, technically nuanced problems.
10 years of coding experience
10 years of employment as a software developer
Certificate Data Analytics, Certificate Data Analytics at Ironhack
Quantum Computing , Quantum Computing at Creative Destruction Lab
Universidad Nacional Autónoma de México (UNAM)
Bachelor's degree Chemistry, Bachelor's degree Chemistry at Universidad de Guadalajara
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