Vish Rajiv
Software Engineer at Weights & Biases
San Francisco Bay Area United States
Summary
🤩
Rockstar🎓
Top SchoolVish Rajiv is a software engineer with eight years of experience based in the San Francisco Bay Area, currently contributing to Weights & Biases' back-end and MLOps efforts. He specializes in artifact management and experiment tracking, having implemented pagination for report retrieval and improved error handling and offline artifact behavior to make model workflows more robust. Comfortable working at the intersection of backend systems and machine learning operations, he focuses on optimizing data flow and reducing network-related issues in production. His open-source contributions to the widely used wandb platform demonstrate a practical, product-focused approach to infrastructure reliability. A New York University alumnus, he blends academic grounding with hands-on engineering in a fast-moving AI tooling company. Colleagues rely on him for pragmatic fixes that improve developer experience and operational stability.
8 years of coding experience
New York University
Github Skills (14)
experiment10
artifact10
python10
back-end-development10
artifacts10
artifactory10
apidoc9
mlops9
api9
error-handling8
data-science7
testing7
pytest7
machine-learning7
Programming languages (5)
Github contributions (5)
wandb/wandb
Aug 2021 - Jan 2023
The AI developer platform. Use Weights & Biases to train and fine-tune models, and manage models from experimentation to production.
Role in this project:
Back-end Developer & MLOps Engineer
Back-end Developer & MLOps EngineerContributions:111 reviews, 63 commits, 42 PRs in 1 year 5 months
Contributions summary:Vish primarily contributed to the Weights & Biases platform's back-end, focusing on artifact management and experiment tracking. They addressed bugs related to artifact usage in offline mode and improved error handling for invalid aliases. Additionally, the user worked on optimizing the report data retrieval process by implementing pagination and addressed a bug related to network usage, demonstrating MLOps skills by improving data flow.
pythoncollaborationtensorflowhyperparameter-tuningcli
vwrj/neural_machine_translation
Nov 2018 - Jun 2019
Contributions:38 pushes in 6 months
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