Industrial Engineering researcher, software engineer, and infrastructure practitioner.

Research Centre on Production Management and Engineering · mmateo@cigip.upv.es · LinkedIn · X/Twitter

I work at the intersection of software engineering, production systems, and infrastructure operations. My background combines academic research in industrial engineering with hands-on experience building, deploying, and operating digital systems.

My main areas of focus are:

• Kubernetes and container platforms in production environments
• DevOps, systems administration, and platform reliability
• Web and application development
• Production, logistics, and supply chain engineering
• Technical leadership and project management

• Project management since 2021
• Kubernetes in production since 2020
• Docker in production since 2018
• Web and app development since 2016
• Systems administration since 2012

• PhD in Industrial Engineering and Production, 2022-2026
• Master's Degree in Advanced Engineering in Production, Logistics and Supply Chain Management, 2021-2022
• Master's Degree in Computer Engineering, 2019-2021
• Computer Science Engineering, 2014-2019
• Advanced Degree in Computer Systems and Network Administration, 2010-2012

Platform and infrastructure
Kubernetes, Docker, Linux, Nginx, Grafana, Vagrant

Backend and scripting
Python, Node.js, Java, PHP, Bash

Frontend and app development
Angular, React, Ionic, Electron, HTML, CSS, Sass, JavaScript, TypeScript

Data and persistence
PostgreSQL, MariaDB, MySQL, MongoDB, Firebase

• Applying engineering research to real production environments
• Designing reliable and maintainable cloud-native systems
• Bridging operations, software delivery, and industrial processes

I have contributed to several European research and innovation initiatives focused on AI, smart manufacturing, industrial data, and resilient production systems:

• ZDMP
  Zero-defect manufacturing platform for data-driven quality optimization, predictive services, and industrial interoperability.
• i4Q
  Industrial data services for quality control in smart manufacturing.
• AIDEAS
  AI-driven industrial equipment lifecycle management to improve agility, sustainability, and resilience across design, manufacturing, use, and end-of-life stages.
• DiMAT
  Digital solutions for more resilient, circular, and sustainable manufacturing value chains.
• MaaSAI
  AI-enabled manufacturing-as-a-service approaches to improve flexibility, intelligence, and responsiveness across industrial ecosystems.

These are the journal articles with the highest-impact venues I could verify from your public research footprint. Where journal impact was close, I prioritized the most cited article I could confirm.

1. Reference architecture for the design and implementation of AI systems in manufacturing in conformity to ISO/IEC 42001
   Published in International Journal of Computer Integrated Manufacturing.
2. Digital assets in zero-defect manufacturing: literature review and proposed framework
   Published in International Journal of Production Research.
3. Towards a Reference Architecture for Machine Learning Operations
   Published in Computers.
4. An industry maturity model for implementing Machine Learning operations in manufacturing
   Published in International Journal of Production Management and Engineering.

More info here