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My expertise includes retrieval-augmented generation, query understanding, prompt engineering, embedding fine-tuning, evaluation workflows, and MLOps observability. I’m especially useful when the problem is not just “which model should we use?” but “how do we build, measure, debug, and improve this system end to end?” As a forward-deployed engineer, I’m comfortable working close to users, product teams, and engineering teams: understanding the real workflow, identifying the highest-leverage technical problems, building the right experiments, and shipping pragmatic solutions. You should consider working with me if your team is building an LLM product and: You don’t know how to improve quality systematically Your RAG or agentic workflows feel unreliable You’re overwhelmed by AI tools, frameworks, and vendor claims You need sharper evals, observability, or experimentation loops You want someone who can bridge product ambiguity and engineering execution
Udacity
Natural Language Processing Nanodegree
January 1, 2018 – January 1, 2019
Stanford University
Deep Learning Specialization
January 1, 2017 – January 1, 2018
Universidad Politécnica de Madrid
Ingeniería de Computadores
January 1, 2013 – January 1, 2017
Anvyr
Forward Deployed Engineer
January 1, 2026 – Present
Provincia de Madrid · Remote
Giza
CTO
November 1, 2024 – December 1, 2025
Giza
Co-Founder & CEO
May 1, 2022 – December 1, 2024
adidas
MLOps Tech Lead
May 1, 2022 – April 1, 2023
Madrid y alrededores
BBVA Next Technologies
MLOps Tech Lead
May 1, 2021 – May 1, 2022
KeepCoding®
MLOps Instructor at Big Data & Machine Learning Bootcamp
April 1, 2020 – May 1, 2022
Madrid, Comunidad de Madrid, España
BBVA Next Technologies
Senior ML Engineer
September 1, 2019 – May 1, 2021
BBVA Next Technologies
Data Scientist
November 1, 2018 – September 1, 2019
Accenture
Data Scientist
September 1, 2017 – November 1, 2018
Madrid y alrededores, España
GMV
Aerospace Software Engineer
July 1, 2017 – August 1, 2017
Madrid y alrededores, España
IXION Industry & Aerospace, S.L
Robotics Engineer
February 1, 2017 – June 1, 2017
Madrid y alrededores, España
Professional Machine Learning Engineer
Google Cloud - Minnesota
June 24, 2026 – Present
AWS Certified Machine Learning – Specialty
Amazon Web Services (AWS)
June 24, 2026 – Present
Associate Cloud Engineer
Google Cloud - Minnesota
June 24, 2026 – Present
AWS Certified Solutions Architect – Associate
Amazon Web Services (AWS)
June 24, 2026 – Present
Natural Language Processing Nanodegree
Udacity
June 24, 2026 – Present
Deep Learning Specialization
Coursera
June 24, 2026 – Present
Sequence Models
Coursera
June 24, 2026 – Present
Convolutional Neural Networks
Coursera
June 24, 2026 – Present
Structuring Machine Learning Projects
Coursera
June 24, 2026 – Present
Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization
Coursera
June 24, 2026 – Present
Neural Networks and Deep Learning
Coursera
June 24, 2026 – Present
B2 English Certificate
TOEIC® Program
June 24, 2026 – Present
Cultural Fit Analysis
The candidate's diverse experience across large enterprises (adidas, BBVA, Accenture), startups (Giza, Anvyr), and educational institutions (KeepCoding) demonstrates adaptability and a broad perspective. Their involvement in cutting-edge areas like ZKML and autonomous agents, combined with a focus on productionizing AI, aligns well with innovative and fast-paced environments. The entrepreneurial background suggests a proactive, ownership-driven mindset. The target role of ML Engineer aligns well with their deep technical and leadership experience in ML/MLOps.
Soft Skills & Operational Fit
The candidate's experience as a CTO and MLOps Tech Lead indicates strong leadership, project management, and strategic thinking. Their freelance work as a Forward Deployed Engineer suggests adaptability, client-facing skills, and a focus on production systems and operational handoff. The instructor role at KeepCoding also points to strong communication and mentoring abilities. However, without psychometric test results, a deeper assessment of work attitude, stress handling, and team collaboration is not possible.