Backend Engineer · Distributed Systems · MSc UC3M
I build real-time platforms and backend services for operational environments. MSc in Computer Science and Technology from UC3M (2026), with industry research experience in data platforms and distributed systems.


September, 2025 — July, 2026
Software Developer — Industry Research Project
MSc completed in July 2026 at Universidad Carlos III de Madrid. As part of my thesis I worked on an industry research project, developing systems and prototypes focused on data platforms and distributed systems.
September, 2023 — December, 2025
I worked on a real-time monitoring platform for nationwide broadcasting infrastructure, designing REST APIs and backend services for asset and incident management and building map-based operational dashboards. I introduced Redis Stack as a caching layer, cutting load times by 50-60%; I built the GitLab CI/CD pipeline from scratch, reducing deployment time by over 85%; and I expanded an internal login module into a company-wide authentication microservice serving hundreds of users.
September, 2020 — November, 2025
Studies completed in November 2025. A stage where I developed my passion for software engineering and contributed to projects worth highlighting:
July, 2019
I completed my pre-university studies at Salesianos Stma. Trinidad in the science track.
Real-time collaborative diagramming application compliant with the SysML v2 standard, developed as part of my MSc thesis at UC3M. Built with React and Next.js on the frontend, an ASP.NET backend over PostgreSQL, and HocusPocus for conflict-free multi-user editing.
Decentralized end-to-end encrypted messaging platform exploring user-owned identity, distributed data storage, and privacy-first social communication.
AI planning toolkit for temporal multi-agent maze problems in PDDL, featuring OPTIC integration, interactive 3D visualization, graph export, and benchmarking.
Pokémon image classifier built on a ResNet18 convolutional network fine-tuned with PyTorch, packaged as an Electron + React desktop app and shipped alongside a Streamlit demo for quick in-browser inference.
Machine learning project for emotion classification in Old Spanish texts, combining NLP, historical language analysis, and model evaluation.