Tejas Chumbalkar
System Development Engineer 2 at Amazon Web Services (AWS)
Palo Alto, California, United States
Summary
🤩
Rockstar🎓
Top SchoolTejas Chumbalkar is a cloud-focused Systems Development Engineer with eight years of experience blending software engineering, DevOps, and ML operations at AWS. He holds an MS in Computer Engineering from San Jose State University and has hands-on background in network security research, DDOS detection, and NFV from his graduate work. At AWS he’s contributed to high-profile open-source projects like AWS Deep Learning Containers and the SageMaker Python SDK, helping enable Kubeflow on EKS, add framework/version support, and implement IAM and instance compatibility fixes. Comfortable across Linux, cloud infrastructure, and model deployment pipelines, he brings a pragmatic, security-minded approach to building scalable testing and CI/CD systems. Based in Palo Alto, he combines enterprise Java application experience with modern cloud-native tooling to solve complex production issues and streamline ML deployments.
8 years of coding experience
2 years of employment as a software developer
San José State UniversityBachelor of Engineering - BE, Electronics and Telecommication, Bachelor of Engineering - BE, Electronics and Telecommication at Vishwakarma Institute of Information Technology
English, Hindi, Marathi
Github Skills (15)
amazon-eks10
pytorch10
machine-learning10
kubernetes10
tensorflow10
aws10
python10
cicd10
kubernetes-pods10
sagemaker10
testing10
huggingface8
git8
dockers5
docker5
Programming languages (6)
Github contributions (5)
aws/deep-learning-containers
Jul 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 & MLOps Engineer
DevOps & MLOps EngineerContributions:1 release, 1308 reviews, 182 commits in 2 years 5 months
Contributions summary:Tejas primarily focused on setting up and configuring the Kubeflow infrastructure for EKS testing within the AWS Deep Learning Containers repository. Their contributions involved modifying test configurations, disabling and enabling tests, and addressing git-related issues. The user also implemented changes to existing test scripts, modifying parameters, and reformatting code. The user was also involved in implementing IAM roles in the AWS EKS cluster.
pytorchsagemakercontainersmxnetserving
aws/sagemaker-python-sdk
Nov 2022 - Dec 2022
A library for training and deploying machine learning models on Amazon SageMaker
Role in this project:ML Engineer
ML EngineerContributions:10 reviews, 3 commits, 8 PRs in 1 month
Contributions summary:Tejas contributed to the AWS SageMaker Python SDK by adding support for new versions of PyTorch and TensorFlow, demonstrating a focus on maintaining compatibility with different machine learning frameworks. They addressed instance type support for Hugging Face tests. The user also added support for TF2.12 SageMaker DLC and addressed P2 instance deprecation. These changes involve modifications to the SDK's core functionality and testing procedures, indicating expertise in deploying machine learning models on Amazon SageMaker.
pytorchsagemakerdeployingmxnetpython
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