Pravali Uppugunduri
SDE II at Amazon Web Services (AWS)
Seattle, Washington, United States
Summary
🤩
Rockstar🎓
Top SchoolPravali Uppugunduri is an SDE II based in Seattle with 7 years of experience building data-driven ML and analytics solutions, currently advancing model deployment capabilities at AWS. She brings a strong academic foundation in data analytics and machine learning from Northeastern University and hands-on expertise across Python, R, SQL, Spark, and NoSQL systems. At AWS she contributed to the widely used SageMaker Python SDK, enhancing in-process deployment modes and inference preprocessing/postprocessing—work that smooths the path from models to production. Her background spans data engineering at Wayfair, enterprise systems at Cognizant, and operations leadership, giving her a practical lens on end-to-end data product delivery. Comfortable with both research-oriented ML (NLP, time series, deep learning) and visualization tools like Tableau and Power BI, she bridges modeling, engineering, and user-facing insights. An active GitHub contributor with AWS-focused projects, she focuses on pragmatic, production-grade ML tooling rather than just prototypes.
7 years of coding experience
5 years of employment as a software developer
Master's degree Data Analytics Engineering, Master's degree Data Analytics Engineering at Northeastern UniversityBachelor's degree Electrical Electronics and Communications Engineering, Bachelor's degree Electrical Electronics and Communications Engineering at GITAM Deemed University
Github Skills (11)
machine-learning10
python10
sagemaker10
pytorch8
tensorflow8
api-design7
api7
docker6
fastapi6
dockers6
triton5
Programming languages (2)
Github contributions (5)
aws/sagemaker-python-sdk
Sep 2024 - Dec 2024
A library for training and deploying machine learning models on Amazon SageMaker
Role in this project:
ML Engineer
ML EngineerContributions:1 review, 3 PRs in 3 months
Contributions summary:Pravali primarily contributed to the development and enhancement of the SageMaker Python SDK, specifically focusing on features related to model deployment and in-process mode support. This included adding functionalities for deployment configuration names, integrating in-process mode for DJL and TorchServe servers, and supporting model IDs. The user also incorporated pre-processing and post-processing logic into the inference spec and added related documentation.
pytorchsagemakerdeployingmxnetpython
pravali96/Data-Analysis-Dashboarding
May 2021 - Aug 2021
Contributions:29 commits, 1 PR, 27 pushes in 2 months
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