Sai Miduthuri
Software Engineer at Anyscale
Mountain View, California, United States
Summary
🤩
Rockstar🎓
Top SchoolSai Miduthuri is a software engineer with six years of experience building scalable ML and vision systems, currently working at Anyscale out of Mountain View. He has deep hands-on expertise in deep learning infrastructure—contributing to high-profile AWS open-source projects like SageMaker Python SDK and Deep Learning Containers to add framework support, Docker images, and distributed training tooling. At AWS he architected multi-region CI/CD, canary testing, and SLO monitoring for DLCs and helped deliver Managed MLFlow integrations, demonstrating both systems-level engineering and MLOps product impact. Earlier work on embedded image-processing and FPGA/ARM codesign shows a strong hardware-software co-design background that informs his approach to efficient model deployment. He holds an MS in Computer Science from Stony Brook and a B.Tech from IIIT Hyderabad.
6 years of coding experience
8 years of employment as a software developer
Bachelor of Technology (B.Tech.) Electrical Electronics and Communications Engineering, Bachelor of Technology (B.Tech.) Electrical Electronics and Communications Engineering at IIIT HyderabadMaster of Science - MS Computer Science, Master of Science - MS Computer Science at Stony Brook University Graduate SchoolIndian School Muscat
Stackoverflow
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1answer
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Github Skills (20)
kubernetes10
pytorch10
docker10
pytest10
python10
testing10
mxnet10
machine-learning10
dockers10
cicd10
sagemaker10
tensorflow10
aws10
build-automation10
kubernetes-pods10
Programming languages (6)
Github contributions (5)
aws/deep-learning-containers
Feb 2020 - Dec 2022
AWS Deep Learning Containers are pre-built Docker images that make it easier to run popular deep learning frameworks and tools on AWS.
Role in this project:
DevOps Engineer & ML Engineer
DevOps Engineer & ML EngineerContributions:1 release, 1810 reviews, 333 commits in 2 years 10 months
Contributions summary:Sai primarily worked on updating Dockerfiles for the AWS Deep Learning Containers repository. They focused on updating the versions for MXNet inference and training environments. Further, the user added and made adjustments to various TF dockerfiles, including the addition of new TensorFlow 2.0 and 2.1 versions. The contributions include incorporating specific tooling like Horovod to enable distributed training.
pytorchsagemakercontainersmxnetserving
aws/sagemaker-python-sdk
Aug 2020 - Aug 2022
A library for training and deploying machine learning models on Amazon SageMaker
Role in this project:ML Engineer
ML EngineerContributions:39 reviews, 16 commits, 13 PRs in 1 year 11 months
Contributions summary:Sai primarily contributed to the Amazon SageMaker Python SDK, focusing on TensorFlow and PyTorch support. They implemented features to integrate the latest versions of these frameworks, including adding support for new releases (e.g., TF 2.5, PT 1.9, PT 1.11) and updating existing support. They also modified related test files to ensure the proper functioning of the SDK with these framework versions.
pytorchsagemakerdeployingmxnetpython
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