Dhanasekar Karuppasamy
Senior Software Development Engineer - Amazon Q at Amazon Web Services (AWS)
California, United States
Summary
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Rockstar🎓
Top SchoolDhanasekar Karuppasamy is a Senior Software Development Engineer at AWS with six years in AI and cloud-native engineering and a decade-plus of overall experience building production systems. He currently helps ship AI-powered developer tools for Amazon Q—features like Inline/IDE Chat, Next Edit Predictions, Code Scan, and a CLI—combining product-facing delivery with deep infrastructure work. Prior roles include maintaining PyTorch TorchServe and integrating SageMaker inference stacks, and contributing DevOps automation to the widely used pytorch/serve project, adding Docker/Conda build customization and regression testing with AWS CodeBuild. He started his career building mapping and WebRTC services at Amazon and holds an MS in Computer Science from University at Buffalo. Known for bridging backend engineering, automation, and ML deployment, he excels at turning research-grade models into robust, scalable developer experiences.
6 years of coding experience
10 years of employment as a software developer
Master of Science (MS) Computer Science, Master of Science (MS) Computer Science at University at BuffaloB.Tech Computer Science and Engineering, B.Tech Computer Science and Engineering at Amrita University
English, Tamil, Hindi, Kannada
Github Skills (20)
kubernetes10
docker10
scripting10
bash10
dockers10
cicd10
automation10
script10
automation-testing10
kubernetes-pods10
sh10
automations10
shell10
testing9
python9
Programming languages (4)
Github contributions (5)
pytorch/serve
Apr 2020 - Jun 2021
Serve, optimize and scale PyTorch models in production
Role in this project:
DevOps Engineer & Automation Engineer
DevOps Engineer & Automation EngineerContributions:2 releases, 98 reviews, 242 commits in 1 year 2 months
Contributions summary:Dhanasekar primarily contributed to infrastructure and build automation, modifying shell scripts for Conda package builds and Docker image creation. They implemented features for Docker image customization, adding options to specify GPU usage and device IDs. Furthermore, they introduced a regression test suite, integrating AWS CodeBuild and Postman tests for inference and management APIs.
cpupytorchpytorch-modelsservingin-production
dhanainme/code-build-test
Jul 2020 - Dec 2020
Contributions:21 pushes, 1 branch in 4 months
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