ESCAEN
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ESCAEN
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Backend Machine learning

Models in production, backends that hold up.

I'm Daniel Galán, an MLOps engineer and backend developer: I take machine learning models to production and build the systems that serve them. Here I show how, through real projects.

Projects

Each project covers the problem, the decisions, the architecture and the numbers.

Machine learning · Master's thesis

Reconstructing cosmic-ray air showers at HAWC

A convolutional network that treats the observatory's sensors as the pixels of an image and predicts where the shower core lands.

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SaaS · In production

Vecinly

SaaS for homeowners' associations in 11 languages: one API for the web and two apps, with each community's data encrypted with its own key.

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In development · Backend and app

Official alerts from 216 public sources, each with who issued it. Server in production and website in 8 languages.

 In production · Website

A B2B website in Spanish and English: 78 pages, a mobile performance score of 100 and no server.
About

MLOps engineer and Java backend developer.

I started out in Java backend: microservices, domain events and integrations with external providers. For my master's in AI I wrote my thesis on real data from the HAWC observatory, and since 2025 I have worked as an ML Engineer. What I enjoy most is what lies in between: making sure a model or a system doesn't stay in the notebook or the demo, but holds up in production.

How I work

  • Agile methods: Scrum and Kanban: short deliveries, reviews with the business and priorities adjusted every sprint.
  • Git and code review: a branch per task, small pull requests and review before merging.
  • CI/CD and testing: every change passes automated tests (unit, and integration with Testcontainers) before it is deployed.
  • Living documentation: architecture decisions written next to the code, so the team knows the why.
Work Education
2025 — now

ML Engineer

Training pipelines, deployment and monitoring of models in the cloud.

  • Python
  • TensorFlow / Keras
  • scikit-learn
  • MLflow
  • GCP · Vertex AI
2019 — 2025

Backend Java

Microservices, domain events and integration with external providers.

  • Java · Spring Boot
  • Kafka
  • PostgreSQL
  • Oracle
  • Docker
  • Kubernetes
2024

Master's in Artificial Intelligence, UNIR

Thesis: a convolutional network to reconstruct cosmic-ray air showers at HAWC.

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2020

Bachelor's in Computer Engineering, UOC / UdA

Want to talk about a project?

I'll reply myself, by email.

Write to me →