Neelam Gehlot
Principal Engineer at Amazon
Seattle, Washington, United States
Summary
🤩
Rockstar🎓
Top SchoolNeelam Gehlot is a Principal Engineer based in Seattle with a decade of experience building scalable cloud and ML-enabled developer tools at Amazon and AWS. She has driven core features for Amazon Creators' attribution and compensation systems while previously helping launch and integrate flagship SageMaker Studio, Notebooks, and related ML tooling. Her work spans full-stack development, MLOps, and developer experience—evidenced by contributions to the popular jupyterlab-git extension and practical improvements to AWS SageMaker example notebooks. Neelam combines product-minded engineering with attention to usability, adding browser-based testing, clear error handling, and resource-optimizing configs to increase reliability for end users. Colleagues know her for shipping cross-team integrations (e.g., BYO container support for Studio) and for improving developer workflows behind high-profile AWS launches. She holds a MS in Computer Science from USC and brings both production-scale systems chops and hands-on open-source contribution experience.
10 years of coding experience
5 years of employment as a software developer
Master's degree, Computer Science, Master's degree, Computer Science at University of Southern CaliforniaBachelor's degree, Computer Engineering, Bachelor's degree, Computer Engineering at Sardar Patel Institute of Technology/ Mumbai University
English, Hindi
Stackoverflow
414reputation
50kreached
15answers
0questions
Github Skills (37)
javascript10
typescript10
jupyterlab10
amazon-sagemaker10
jupyterlab-extension10
front-end-development10
typescript-types10
typescripts10
react10
git9
python9
jestjs9
docker8
dockers8
testing8
Programming languages (5)
Github contributions (5)
jupyterlab/jupyterlab-git
Oct 2018 - May 2019
A Git extension for JupyterLab
Role in this project:
Full-stack Developer
Full-stack DeveloperContributions:14 commits, 15 PRs, 19 comments in 7 months
Contributions summary:Neelam primarily focused on enhancing the user interface and functionality of the JupyterLab Git extension. They implemented and tested frontend components like the Branch Header, and also added features such as modal confirmation for destructive actions and error messages for user feedback. The user also addressed build failures and updated the test setup to improve the overall quality and usability of the extension. Furthermore, the user added browser-based testing to ensure proper operation of the extension in a browser environment.
git-extensionversion-controljupyterlab-extensionjupyterlabjupyter
aws/amazon-sagemaker-examples
Feb 2019 - Mar 2019
Example 📓 Jupyter notebooks that demonstrate how to build, train, and deploy machine learning models using 🧠Amazon SageMaker.
Role in this project:MLOps Engineer
MLOps EngineerContributions:8 commits, 20 PRs, 9 pushes in 1 month
Contributions summary:Neelam's contributions focused on improving the usability and robustness of example notebooks within the Amazon SageMaker ecosystem. They implemented changes to setup scripts, ensuring they handled user access and provided clear feedback. The user also updated notebook configurations, specifically instance types, to optimize resource utilization and address capacity issues. These changes highlight a focus on making the examples more user-friendly and efficient in the context of SageMaker.
pythonjupyter-notebooktrainingawssagemaker
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