Daniel Souza
Full Stack Engineer at LiveFlow
Santa Catarina, Brazil
Summary
🤩
Rockstar🎓
Top SchoolDaniel Souza is a seasoned software engineer and technology leader with 11 years of experience building scalable AI and cloud-native systems from Brazil's Santa Catarina. He has led engineering and AI teams in healthcare, delivering mission-critical, privacy-conscious speech recognition solutions that run cloud, on-premise, and desktop, and he fine-tuned custom ASR and LLM-based pipelines for multilingual clinical contexts. As a full-stack engineer at LiveFlow he designed high-concurrency financial modules and real-time bank feed integrations using Elixir, Phoenix and React, emphasizing idempotent sync and rigorous testing. Daniel is an active contributor to open-source speech-to-text tooling (notably coqui-ai/STT), where he improved native client interfaces and model loading from buffers to make model usage more efficient. Comfortable operating in the gap between “it works on my machine” prototypes and production-grade systems, he combines hands-on implementation with architectural leadership to ship reliable, auditable software for regulated domains.
11 years of coding experience
Master of Engineering - MEng Microelectronics and Automation, Master of Engineering - MEng Microelectronics and Automation at Polytech MontpellierBachelor of Engineering (B.E.) Computer Engineering, Bachelor of Engineering (B.E.) Computer Engineering at Federal University of Rio Grande do Sul
Portuguese, French, English, Spanish, German
Github Skills (7)
speech-to-text10
c-language10
cprogramming-language10
swig10
javascript9
nodejs9
tensorflow8
Programming languages (7)
Github contributions (5)
coqui-ai/STT
Jul 2022 - Aug 2022
🐸STT - The deep learning toolkit for Speech-to-Text. Training and deploying STT models has never been so easy.
Role in this project:
Back-end Developer
Back-end DeveloperContributions:8 reviews, 8 commits, 8 PRs in 29 days
Contributions summary:Daniel primarily contributed to the speech-to-text (STT) project by modifying the native client code and SWIG interface files. Their work involved supporting model creation from buffer and adapting the codebase to handle data passed as Uint8Array. They also addressed issues in the SWIG typemaps, including reverting and fixing parameters, along with updating release URLs. The commits show significant involvement with the project's core functionality, enabling developers to load and utilize models more efficiently.
voice-recognitionasrspeech-recognitiontrainingspeech-recognition-api
iarahealth/adapters
Jul 2023 - Mar 2025
Editor adapters for the Iara Speech SDK
Contributions:11 releases, 183 reviews, 285 PRs in 1 year 9 months
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