Harsha Balluru
DevOps Tech Lead at Hilton
Collierville, Tennessee, United States
Summary
🤩
Rockstar🎓
Top SchoolHarsha Balluru is a DevOps Tech Lead based in Collierville, Tennessee, with a decade-plus career blending infrastructure architecture, automation and operations across enterprises like Hilton, AWS, FedEx and Qualcomm. He moves fluidly between development and operations, driving CI/CD, containerization and cloud-native practices while obsessing over automation to reduce toil. At AWS he contributed to the widely used Deep Learning Containers—patching TensorFlow, securing images and enabling CloudWatch logging for PyTorch in SageMaker—highlighting hands-on ML infra experience. He has deep middleware and WebLogic roots from earlier roles, giving him uncommon depth in both legacy enterprise stacks and modern cloud platforms. Known for continuously exploring new technologies, he pairs pragmatic delivery with a penchant for simplifying long-running manual processes through tooling and orchestration.
3 years of coding experience
14 years of employment as a software developer
Master's degree Electrical & Computer Engineering, Master's degree Electrical & Computer Engineering at New Jersey Institute of TechnologyBachelor's degree Electronics & communication Engineering, Bachelor's degree Electronics & communication Engineering at Visvesvaraya Technological University
Github Skills (12)
docker10
tensorflow10
aws10
dockers10
cicd9
python9
sagemaker9
pytorch9
protobuf8
protobuffer8
kubernetes6
kubernetes-pods6
Programming languages (2)
Github contributions (5)
aws/deep-learning-containers
Sep 2022 - Jan 2023
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:64 reviews, 17 commits, 70 PRs in 3 months
Contributions summary:Harsha primarily focused on maintaining and improving the AWS Deep Learning Containers. Their contributions included patching TensorFlow versions, addressing security vulnerabilities, and updating Dockerfiles for both CPU and GPU environments. They also implemented and verified Cloudwatch logging for PyTorch models within the SageMaker ecosystem. Additionally, the user handled build and release processes for TensorFlow serving and made changes to the configuration files and tests.
pytorchsagemakercontainersmxnetserving
hballuru/deep-learning-containers
Sep 2022 - Jan 2023
AWS Deep Learning Containers (DLCs) are a set of Docker images for training and serving models in TensorFlow, TensorFlow 2, PyTorch, and MXNet.
Contributions:283 pushes, 45 branches in 4 months
containerspytorchmxnetservingcaffe2
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