Matheus Aeroso
About Matheus Aeroso
Matheus Aeroso is a Software Engineer currently working at EBANX in Curitiba, Brazil, where he has been employed since 2021. He holds a Bachelor of Science in Computer Science from Universidade Anhembi Morumbi and has extensive experience in developing anti-fraud systems and distributed applications.
Work at EBANX
Matheus Aeroso has been employed as a Software Engineer at EBANX since 2021. He works in a hybrid model from Curitiba, Paraná, Brazil. In this role, he leads the technical efforts for transactional antifraud features on a payment processing platform, which manages millions of transactions daily. His responsibilities include implementing risk analysis modes and load balancing to enhance the reliability of the anti-fraud engine.
Education and Expertise
Matheus Aeroso holds a Bachelor of Science in Computer Science from Universidade Anhembi Morumbi, where he studied from 2021 to 2023. He also earned a Bachelor of Science in Computer Information Systems from the Federal University of Technology - Parana, completing his studies from 2014 to 2019. Additionally, he attended Pontifícia Universidade Católica do Paraná, where he studied iOS Development at the Apple Developer Academy from 2015 to 2016. His technical expertise includes programming languages and frameworks such as Elixir, PHP, Python, React, and Rails.
Professional Background
Before joining EBANX, Matheus Aeroso worked as a Software Engineer at Advocacia Correa de Castro & Associados from 2019 to 2021. He also held positions at Unify as a Software Engineer from 2018 to 2019 and as a Software Engineer Intern from 2017 to 2018. His early career included internships at CINQ Technologies and Projevias - Projetos e Consultorias, where he gained experience in software engineering and development.
Achievements in Software Engineering
Matheus Aeroso has contributed to significant projects in the software engineering field. He collaborated with a major tech company to develop an anti-fraud product utilizing large language models. He has optimized partner integrations, reduced technical debt, and introduced new features to improve internal machine learning models for fraud prevention. His work has focused on enhancing the performance and reliability of payment processing systems.