Rohan Gujarathi
Senior Software Engineer at Amazon Web Services (AWS)
Vancouver, British Columbia, Canada
Summary
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Rockstar🎓
Top SchoolRohan Gujarathi is a Senior Software Engineer based in Vancouver with nine years of experience building Java and Python systems, web applications, and ML tooling. He has progressed through multiple engineering tiers at AWS, where he now focuses on delivering production-ready solutions and leading projects to timely completion. Hands-on MLOps work includes notable contributions to the widely used aws/sagemaker-python-sdk, adding pipeline, Clarify, debugger, and LambdaStep test integrations that strengthen end-to-end validation. A proactive problem-solver and mentor, he combines strong debugging and root-cause analysis skills with a track record of collaborating effectively in large, structured corporations. With an MS in Computer Science and a background at Infosys and Amazon internships, he brings both rigorous academic training and practical enterprise experience.
9 years of coding experience
3 years of employment as a software developer
Bachelor of Engineering (B.E.) Computer Science, Bachelor of Engineering (B.E.) Computer Science at Thakur College Of Engineering and TechnologyUMBC
English, Hindi, Marathi, gujrathi
Stackoverflow
1reputation
0reached
0answers
0questions
Github Skills (12)
machine-learning10
mlops10
aws10
python10
pipeline10
sagemaker10
cicd9
testing9
docker8
dockers8
pytorch6
tensorflow6
Programming languages (4)
Github contributions (5)
aws/sagemaker-python-sdk
Dec 2020 - Aug 2021
A library for training and deploying machine learning models on Amazon SageMaker
Role in this project:
MLOps Engineer
MLOps EngineerContributions:37 reviews, 3 commits, 14 PRs in 7 months
Contributions summary:Rohan's commits primarily focus on integrating and testing SageMaker pipelines, specifically those involving Clarify and debugger tools. They modified existing tests and added new ones to incorporate these functionalities, demonstrating a focus on end-to-end workflow validation within the SageMaker environment. Their work included updating tests, handling file paths, and integrating new LambdaStep support within the pipeline. The user also fixed remote function related issues.
amazon-sagemakermachine-learning-modelsawsmxnettensorflow
rohangujarathi/SOCProject
Oct 2018 - Mar 2019
Contributions:20 pushes, 1 branch in 4 months
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