Alonso Fernández
Senior Research Software Development Engineer at Microsoft
Costa Rica
Summary
🤩
Rockstar🎓
Top SchoolAlonso Fernández is a Senior Research Software Development Engineer with a decade-plus background building large-scale, cloud-native applications and leading engineering teams across Amazon and Microsoft. He combines hands-on system design on AWS with research-oriented software development, contributing to notable open-source projects like Microsoft’s graphrag RAG system where he improved core autotuning and reliability. Based in Costa Rica, he pairs production delivery experience in distributed and serverless architectures with an academic track record as a professor of software and mobile development, mentoring master's students and teaching Android and web courses. Known for disambiguating complex problems and modeling ML and third‑party interactions, he is focused on deepening his software architecture expertise and scaling leadership impact.
4 years of coding experience
11 years of employment as a software developer
Master's degree, Computer Science, Master, Master's degree, Computer Science, Master at Universidad de Costa RicaBachelor's degree, Computer Science, Bachelor's degree, Computer Science at Tecnológico de Costa RicaMicromaster, Statistics and Data Science, Micromaster, Statistics and Data Science at MITx on edX
Spanish, English
Github Skills (8)
ag10
lm10
gpt10
python10
llm10
configuration-management9
testing8
github7
Programming languages (3)
Github contributions (5)
microsoft/graphrag
Jul 2024 - Mar 2025
A modular graph-based Retrieval-Augmented Generation (RAG) system
Role in this project:
Back-end Developer
Back-end DeveloperContributions:22 releases, 372 reviews, 413 PRs in 8 months
Contributions summary:Alonso primarily contributed to the core functionality of the Retrieval-Augmented Generation (RAG) system. They fixed issues related to autotuning and parameter handling, and implemented support for new parameters. The user also made changes to the configuration, codebase, and dependencies. They addressed various bugs and enhancements, indicating a focus on improving the reliability and capabilities of the system.
gptgpt-4gpt4graphragllm
arjun-234/graphrag
Jul 2024 - Aug 2024
A modular graph-based Retrieval-Augmented Generation (RAG) system
Contributions:2 pushes in 1 month
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