Amog Kamsetty
Angel Investor & Advisor at ray-project anyscale
San Jose, California, United States
Summary
🤩
Rockstar🎓
Top SchoolAmog Kamsetty is an engineer and angel investor with 11 years of experience building distributed AI infrastructure and scalable ML tooling from research to production. As an early engineering hire at Anyscale and a top committer to the Ray project, he co-created Ray Train and drove core work on Ray Data that power distributed training and large-scale data processing at companies like Uber, Spotify, and Amazon. He blends product ownership, customer engagement, and hands-on backend/DevOps work—improving performance, CI, and documentation across major open-source systems. Based in San Jose, he advises stealth and AI startups (including Sutro and Viveka) and has recent industry experience in GenAI roles at Character.AI and OpenAI. A Berkeley MS/BS alumnus and former RISE Lab researcher, he brings a rare mix of academic rigor and production-savvy engineering to large-scale ML systems.
11 years of coding experience
5 years of employment as a software developer
Master of Science - MS, Computer Science, Master of Science - MS, Computer Science at University of California, BerkeleySaint Francis High School
English, Spanish
Stackoverflow
184reputation
7kreached
4answers
1question
Github Skills (14)
ray10
python10
cicd10
data-pipelines9
batch-processing9
tensorflow9
data-pipeline9
pytorch9
documentation8
huggingface-transformers6
ray-tune6
redis6
postgresql6
classification6
Programming languages (7)
Github contributions (5)
ray-project/ray
Jan 2020 - Jan 2023
Ray is an AI compute engine. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.
Role in this project:
Back-end & DevOps Engineer
Back-end & DevOps EngineerContributions:2 releases, 2919 reviews, 413 commits in 3 years
Contributions summary:Amog contributed to the Ray project by modifying Travis CI scripts and re-arranging the existing documentation for better clarity. They also worked on various enhancements in the Distributed Processing and Air Train, contributing to the addition of a new data format for PyTorch and the support for batch processing. The user's work focused on improving the infrastructure and performance of the platform.
pythonconsistsruntimetensorflowserving
amogkam/batch-inference-benchmarks
Jan 2023 - Oct 2024
Contributions:2 reviews, 1 PR, 20 pushes in 1 year 8 months
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