Callis Ezenwaka
Software Engineer (Partnership) at OpenMined
London, England, United Kingdom
Summary
🤩
Rockstar🎓
Top SchoolCallis Ezenwaka is a software engineer with 6+ years delivering cloud-native enterprise applications and a broader 8-year technical background spanning biomedical engineering, fintech, and blockchain integrations. Skilled in full-stack development, CI-driven documentation (Sphinx/readTheDocs), API integrations, and UI polish—he has shipped product features that improved MAUs and cut support requests while optimizing backend performance. At OpenMined he blends engineering with privacy research, contributing published work on Privacy-Enhancing Technologies for collaborative data access and implementing UX features like dark mode for Syft notebooks. Practical and data-informed, he has driven AI and automation rollouts at GE that reduced vendor spend and operational arrears, and contributed documentation fixes to Microsoft’s ML learning notebooks. Based in London, he combines research curiosity with product-focused execution and a track record of turning operational data into actionable digital transformation.
8 years of coding experience
4 years of employment as a software developer
MSc Biomedical Engineering Biomedical/Medical Engineering, MSc Biomedical Engineering Biomedical/Medical Engineering at University of HullBachelor of Engineering - B.Eng Biomedical Engineering, Bachelor of Engineering - B.Eng Biomedical Engineering at University of Ilorin, Nigeria
English, Igbo, French, German
Stackoverflow
1reputation
0reached
0answers
0questions
Github Skills (4)
jupyter-notebook10
documentation10
machine-learning5
python4
Programming languages (14)
Github contributions (5)
MicrosoftDocs/ml-basics
Mar 2021 - Mar 2021
Exercise notebooks for Machine Learning modules on Microsoft Learn
Role in this project:
Technical Writer
Technical WriterContributions:5 commits, 5 PRs in 2 days
Contributions summary:Callis primarily contributed to the repository by correcting grammatical errors and fixing typos within the exercise notebooks. Their work focused on improving the clarity and accuracy of the documentation, specifically in the context of machine learning concepts. These modifications included updates in several Jupyter notebooks related to data exploration, regression, and transfer learning, demonstrating a focus on ensuring the quality of learning materials. Their contributions are limited to textual edits, thus helping improve the existing documentation.
data-sciencemachinelearningmachine-learningnotebooksmicrosoft
callezenwaka/Bioinformatics
Nov 2019 - Dec 2024
Contributions:6 pushes, 1 branch in 5 years 2 months
Find and Hire Top DevelopersWe’ve analyzed the programming source code of over 60 million software developers on GitHub and scored them by 50,000 skills. Sign-up on Prog,AI to search for software developers.