Shivam Khare
Member Of Technical Staff at xAI
San Francisco Bay Area United States
Summary
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Rockstar🎓
Top SchoolShivam Khare is a machine learning and systems-focused software engineer with six years of experience building scalable AI infrastructure and real-time vision systems, currently a Member of Technical Staff at xAI in the San Francisco Bay Area. He blends research and production engineering—having worked on embedded computer vision at Samsung, contributed deadline-aware queuing and SLO support to the high-profile Ray distributed AI runtime, and served as a senior engineer at Twitter. Pursuing a master’s in Computer Science at Georgia Tech, he has research experience in optical graph recognition and category-theory-based deep learning and has taught graduate-level behavioral imaging. Comfortable across backend, DevOps, and research roles, Shivam brings an uncommon combination of agentic LLM infrastructure reliability work and hands-on systems delivery.
6 years of coding experience
8 years of employment as a software developer
Indian Institute of Technology Delhi (IIT Delhi)Master's degree, Computer Science, Master's degree, Computer Science at Georgia Institute of Technology
Stackoverflow
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0reached
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Github Skills (10)
ray10
distributed-systems10
python10
serve9
http-api9
parallel8
deploying8
kubernetes8
kubernetes-pods8
pytest8
Programming languages (4)
Github contributions (5)
ray-project/ray
Dec 2019 - Dec 2020
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 Developer & DevOps Engineer
Back-end Developer & DevOps EngineerContributions:6 reviews, 6 commits, 11 PRs in 1 year
Contributions summary:Shivam primarily focused on enhancing the Ray Serve module by implementing deadline awareness for queries and integrating a pluggable queuing policy. They modified the queueing mechanisms, including the introduction of the `blist` sorted-list implementation for managing deadlines, and added example code demonstrating the new features. In addition, the user contributed to the CI/CD setup by adding required dependencies like `blist` in the `install-dependencies.sh` script, suggesting DevOps contribution. Furthermore, they added support for both relative and absolute Service Level Objectives (SLOs) via HTTP requests.
pythonconsistsruntimetensorflowserving
alindkhare/ray
Sep 2019 - Apr 2021
A fast and simple framework for building and running distributed applications.
Contributions:4 reviews, 7 PRs, 598 pushes in 1 year 7 months
distributed-applicationssimple-frameworkframeworkdistributed-systemsmicroservices
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