Pedro Cabral
Machine Learning Engineer at Hapvida NotreDame Intermédica
Ceará, Brazil
Summary
👤
Senior🎓
Top SchoolPedro Cabral is a Machine Learning Engineer and Computer Scientist with over 10 years of professional experience and 15 years of programming, specializing in production ML and data engineering for Brazil’s largest enterprises. He has built and deployed scalable ML workspaces (Databricks, MLflow, Unity Catalog), delivered 30+ clinical classification models that process 100k+ reports monthly with >95% accuracy, and engineered SLM/DSL-powered pipelines that automated millions of reais in orders. Pedro moves fluidly between data science, data engineering and software engineering—designing APIs with FastAPI, optimizing ML infrastructure costs via autoscaling and policies, and integrating complex systems like SAP and Salesforce. A former Wolfram contributor and maker of looqGPT-style search experiences, he combines deep technical rigor with a knack for translating messy, high-volume business processes into reliable data products.
9 years of coding experience
3 years of employment as a software developer
Bachelor's degree Computer Science, Bachelor's degree Computer Science at Federal University of CearaMathematics and Computer Science, Mathematics and Computer Science at Wolfram High School Summer Research ProgramHigh School Diploma High School/Secondary Diplomas and Certificates, High School Diploma High School/Secondary Diplomas and Certificates at Organização Educacional Farias BritoBachelor's degree Computer Science, Bachelor's degree Computer Science at Universidade de Fortaleza
Portuguese, English
Github Skills (59)
api-rest10
hoppscotch10
translation10
ecosystem10
insomnia10
api-client10
testing10
http10
websocket10
http-client10
api10
rest10
vue10
wails10
graphql10
Programming languages (8)
Github contributions (5)
cabralski/cabralski
Jul 2020 - Sep 2024
Contributions:21 pushes, 1 branch in 4 years 2 months
PCabralSoftware/NBodyNN
Jul 2019 - May 2020
The project goal is to predict the N-Body Simulations outputs and their chaoticity using neural networks and vectorization methods.
Contributions:2 PRs, 18 pushes, 2 branches in 10 months
goaloutputsbodyn-bodymachine-learning
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