Balaji Veeramani
Software Engineer at Anyscale
San Francisco, California, United States
Summary
🤩
Rockstar🎓
Top SchoolBalaji Veeramani is a software engineer with nine years of experience focused on distributed systems and ML infrastructure, currently contributing to Ray at Anyscale in San Francisco. He brings hands-on production expertise from multiple AWS internships where he cut model hosting costs dramatically and redesigned serialization APIs for the SageMaker SDK. A former UC Berkeley CS & Statistics student and Berkeley RISE Lab maintainer of NumS, he has practical experience shipping scalable NumPy-like systems, automated release pipelines, and strict CI/CD quality gates. His open-source work spans core Ray submodules (Train and RLlib) and serializer/deserializer implementations for SageMaker, reflecting a deep blend of backend systems, ML tooling, and developer ergonomics. Notably, he has a track record of turning research-grade systems into production-ready components that improve performance and lower operational cost.
9 years of coding experience
2 years of employment as a software developer
Bachelor's degree, Computer Science and Statistics, 3.962 GPA, Bachelor's degree, Computer Science and Statistics, 3.962 GPA at University of California, BerkeleyMathematics, 4.0 GPA, Mathematics, 4.0 GPA at University of Wisconsin-Madison
Stackoverflow
36reputation
335reached
1answer
0questions
Github Skills (17)
json10
python10
machine-learning10
data-serialization10
numpy10
sagemaker10
serialization10
deserialization10
documentation9
csv9
refactoring9
multiprocessing6
ray6
tensorflow6
multithreading6
Programming languages (5)
Github contributions (5)
ray-project/ray
Dec 2021 - Jan 2023
Ray is an AI compute engine. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.
Role in this project:
Back-end Developer
Back-end DeveloperContributions:1 release, 1461 reviews, 139 commits in 1 year 1 month
Contributions summary:Balaji contributed to the Ray Train and RLlib submodules, enhancing their functionality and addressing issues within the codebase. They clarified docstrings within the session class, converting TrainingResult to a dataclass, and monkeypatched environment variables in a callback test. Additionally, the user added a new PrintCallback for training results and made improvements to the documentation regarding code style.
pythonconsistsruntimetensorflowserving
aws/sagemaker-python-sdk
Jul 2020 - Aug 2021
A library for training and deploying machine learning models on Amazon SageMaker
Role in this project:ML Engineer
ML EngineerContributions:15 reviews, 36 commits, 66 PRs in 1 year 1 month
Contributions summary:Balaji's commits primarily focused on adding and modifying serializers and deserializers for handling data within the SageMaker Python SDK. They introduced base classes for serializers and deserializers and implemented specific implementations, including those for NumPy arrays, JSON, CSV, and JSON Lines formats. The changes involve updates to core components of the SDK related to data handling for model deployment and inference, demonstrating a strong understanding of data serialization and deserialization processes within the context of machine learning workflows.
pytorchsagemakerdeployingmxnetpython
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