Jagadeesh Jaganathan
Software Consultant (Contract) at Lean Apps
Chennai, Tamil Nadu, India
Summary
🤩
Rockstar🎓
Top SchoolJagadeesh Jaganathan is a software consultant with 7 years’ experience helping teams accelerate ML model delivery by building MLOps pipelines and cloud-native infrastructure across AWS, Azure, and GCP. He specializes in automating end-to-end ML workflows with Kubeflow Pipelines, MLflow, and CI/CD, and in serving scalable inference with PyTorch Serve, including gRPC and KServe v2 protocol support. His contributions to high-profile open-source projects like pytorch/serve, kserve, and kubeflow/pipelines reflect a practical focus on containerized deployments, integration testing, and production-ready orchestration. Prior roles span embedded systems and IoT firmware to senior engineering positions, giving him uncommon depth in both low-level systems and cloud-native ML operations. Based in Chennai, he combines hands-on DevOps and backend engineering with a knack for streamlining the training-to-deployment lifecycle so teams can concentrate on experimentation.
7 years of coding experience
9 years of employment as a software developer
Bachelor's degree, Electrical, Electronics and Communications Engineering, Bachelor's degree, Electrical, Electronics and Communications Engineering at Paavai Engineering CollegeSt Johns Matriculation Hr Sec School
English, Tamil
Github Skills (22)
kubernetes10
pytorch10
docker10
python10
kubeflow10
machine-learning10
dockers10
kss10
mlops10
kubernetes-pods10
devops10
grpc9
rest-api8
mle8
ml8
Programming languages (11)
Github contributions (5)
kserve/kserve
Nov 2020 - Jan 2023
Standardized Serverless ML Inference Platform on Kubernetes
Role in this project:
MLOps Engineer
MLOps EngineerContributions:28 reviews, 25 commits, 28 PRs in 2 years 2 months
Contributions summary:Jagadeesh primarily contributed to the integration of TorchServe, a model serving platform, within the KServe ecosystem. Their work involved implementing custom TorchServe servers, adding model archive and config generators, and integrating logging and monitoring capabilities. The user also addressed issues related to deployment and configuration, and added e2e tests for torchserve, demonstrating a focus on the end-to-end deployment and operational aspects of machine learning models.
xgboostsklearnserverlessclient-goknative
pytorch/serve
Oct 2020 - Jan 2023
Serve, optimize and scale PyTorch models in production
Role in this project:Back-end & DevOps Engineer
Back-end & DevOps EngineerContributions:36 reviews, 142 commits, 51 PRs in 2 years 3 months
Contributions summary:Jagadeesh's contributions primarily revolve around modifying and configuring the Docker images and entry points for the KFServing deployment within the PyTorch Serve repository. They modified Dockerfiles and entrypoint scripts, and also introduced a build script for creating KFServing TorchServe images, demonstrating a focus on building and deploying containerized applications. Furthermore, the user added support for the gRPC protocol and added the capability to handle the KServe v2 protocol, showing strong skills in API integration and the expansion of the existing service.
cpupytorchpytorch-modelsservingin-production
Find and Hire Top DevelopersWe’ve analyzed the programming source code of over 60 million software developers on GitHub and scored them by 50,000 skills. Sign-up on Prog,AI to search for software developers.