Jonathan Makunga
Software Engineer at Amazon Web Services (AWS)
Seattle, Washington, United States
Summary
🤩
Rockstar🎓
Top SchoolJonathan Makunga is a software engineer based in Seattle with six years of experience building reliable, production-grade systems at AWS and in insurance technology. He blends hands-on engineering with a security-minded approach, contributing to the SageMaker Python SDK by improving GPU detection, adding telemetry for deployments, and hardening test coverage—work that directly improves ML deployment observability and resource utilization. Comfortable across backend services, QA automation, and cloud tooling, he has practical experience shipping features at scale and automating UI tests earlier in his career. A lifelong learner and former teaching assistant, he pairs curiosity with disciplined engineering practices to turn complex requirements into tested, measurable outcomes.
6 years of coding experience
1 year of employment as a software developer
Bachelor of Science - BS, Information Technology, Bachelor of Science - BS, Information Technology at Northern Kentucky University
Stackoverflow
1reputation
0reached
1answer
0questions
Github Skills (10)
aws10
python10
sagemaker10
tensorflow9
machine-learning9
pytorch9
unit-testing8
integration-testing8
file-upload6
angular6
Programming languages (6)
Github contributions (5)
aws/sagemaker-python-sdk
Jan 2024 - Aug 2024
A library for training and deploying machine learning models on Amazon SageMaker
Role in this project:
ML Engineer
ML EngineerContributions:127 reviews, 55 PRs, 96 comments in 7 months
Contributions summary:Jonathan implemented and improved GPU hardware detection within the SageMaker Python SDK, enhancing the library's ability to manage and utilize GPU resources. This involved modifying code to detect GPU counts and memory sizes on instances. The user also added unit and integration tests and addressed review comments to refine the feature. Another significant contribution was the addition of telemetry metrics for model deployment, including latency and failure events, improving monitoring capabilities.
pytorchsagemakerdeployingmxnetpython
makungaj1/sagemaker-python-sdk
Jan 2024 - Jan 2025
A library for training and deploying machine learning models on Amazon SageMaker
Contributions:6 PRs, 478 pushes, 88 branches in 11 months
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