Navin Soni
SDE II At Amazon SageMaker at Amazon Web Services (AWS)
Bothell, Washington, United States
Summary
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Rockstar🎓
Top SchoolNavin Soni is an accomplished SDE II at Amazon SageMaker with 12 years of experience building low-level firmware, device drivers, and cloud-native ML tooling. He blends C/C++ systems expertise from embedded and driver work with Python and DevOps automation to streamline CI/CD and deployment—evidenced by contributions to the popular aws/sagemaker-python-sdk that improved pre-push hooks, commit tracking in CodePipeline, and secure uploads. Navin’s background spans embedded IoT platforms, protocol gateways, and secure data pipelines to large-scale services at AWS and Alexa, giving him a rare full-stack view from silicon to cloud. He consistently focuses on reliability and reproducible builds, automating testing and encoding fixes like UTF-8 handling for local mode. Based in Bothell, WA, he pairs an MS in Computer Science with hands-on engineering that favors pragmatic, production-first solutions. Colleagues rely on him to bridge device-level constraints and cloud deployment realities to deliver robust, secure systems.
12 years of coding experience
9 years of employment as a software developer
MS, Computer Science, 3.5, MS, Computer Science, 3.5 at Clemson UniversityB.E., Electronics, B.E., Electronics at Shree Ramdeobaba Kamla Nehru Engineering College
Github Skills (8)
automation10
automations10
aws10
python10
cicd10
git9
docker8
dockers8
Programming languages (9)
Github contributions (5)
aws/sagemaker-python-sdk
Aug 2021 - Jan 2023
A library for training and deploying machine learning models on Amazon SageMaker
Role in this project:
DevOps Engineer & Automation Engineer
DevOps Engineer & Automation EngineerContributions:307 reviews, 28 commits, 153 PRs in 1 year 4 months
Contributions summary:Navin contributed to the automation and improvement of the CI/CD pipeline by adding a pre-push Git hook. They also updated the code to get the commit ID in CodePipeline, and added encryption settings to the tar_and_upload_dir method. The user focused on automating the testing and building of the project, and ensuring the correct use of commit information. The user worked on a code that updates localmode to decode urllib response as UTF8.
amazon-sagemakermachine-learning-modelsawsmxnettensorflow
navinsoni/sagemaker-python-sdk
Sep 2021 - Mar 2023
A library for training and deploying machine learning models on Amazon SageMaker
Contributions:7 PRs, 67 pushes, 12 branches in 1 year 6 months
deployingsagemakeramazon-sagemakerdeploying-machine-learningamazon
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