Ravi Mallajosyula
Software Engineer at Google
San Jose, California, United States
Summary
🤩
Rockstar🎓
Top SchoolRavi Mallajosyula is a software engineer with seven years of experience building cloud and ML infrastructure, currently based in San Jose and working at Google. He has deep hands-on experience in cloud IaaS from his years at Oracle and in embedded and systems software from Qualcomm. At Google he contributes to production-grade tooling and has notable open-source contributions to flagship TensorFlow projects, managing release processes and GPU-enabled Docker builds to ensure smooth ML model serving and CI/CD on diverse platforms including Apple Silicon. Ravi combines release engineering, DevOps, and systems expertise to bridge developer workflows and production deployments, with a pragmatic focus on dependency and build correctness. He holds an MS in Computer Engineering from NC State and a B.Tech in Electronics and Communication, reflecting a solid hardware-to-software background. Colleagues describe him as the kind of engineer who quietly stabilizes complex pipelines so teams can ship reliably.
7 years of coding experience
7 years of employment as a software developer
Masters of Science, Computer Engineering, Masters of Science, Computer Engineering at North Carolina State UniversityBachelor of Technology, Electronics and Communication Engineering, Bachelor of Technology, Electronics and Communication Engineering at Visvesvaraya National Institute of Technology
Github Skills (18)
docker10
python10
machine-learning10
dockers10
cicd10
release-management10
deeplearning-ai10
deep-learning10
tensorflow10
gpu10
build-automation10
cuda10
serve10
devops10
documentation10
Programming languages (4)
Github contributions (5)
tensorflow/serving
Jan 2022 - Nov 2022
A flexible, high-performance serving system for machine learning models
Role in this project:
DevOps Engineer
DevOps EngineerContributions:53 releases, 2 reviews, 88 commits in 10 months
Contributions summary:Ravi's contributions primarily focused on configuring and updating the Dockerfile.gpu for the tensorflow/serving repository. Their work involved adding and updating NVIDIA repository keys and package versions, specifically related to TensorRT and CUDA. These changes ensure the correct dependencies are installed for GPU acceleration, contributing to the building and deployment process for machine learning models.
cpppythonservingdeep-learningml
tensorflow/tensorflow
May 2023 - Mar 2025
An Open Source Machine Learning Framework for Everyone
Role in this project:DevOps Engineer & Release Manager
DevOps Engineer & Release ManagerContributions:2 reviews, 42 PRs, 1 push in 1 year 10 months
Contributions summary:Ravi primarily focused on updating and maintaining the TensorFlow release process and associated dependencies. They made multiple commits to update the TensorFlow version across various configuration files, including the version number in `tensorflow.bzl`, `version.h`, and `setup.py`. Additionally, the user adjusted the CI/CD configuration for Apple Silicon builds, and updated tensorboard and keras nightly dependencies. Furthermore, they made changes related to bazel builds in CI/CD pipelines.
pythondata-sciencedeep-learningmlmachine-learning
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