Rajat Mhetre is a Senior Machine Learning Engineer based in Seattle with 11 years of experience building and scaling production ML and data systems across startups and enterprise environments. He has hands-on expertise in distributed training, data engineering, MLOps and event-driven microservices—having trained billion-parameter models on GPU clusters and led teams to build an infinitely scalable, fault-tolerant Lakehouse for terabytes of data. As a founding engineer he designed recommendation and personalization systems that reached millions of daily recommendations and 100K users in a real-time parking search product, and he has productionized models on Kubernetes and TensorFlow Serving. His background spans credit-risk modeling, crypto ledger graph analysis, and cost-optimized DataLake migrations that materially reduced maintenance and reporting costs. Comfortable in both Python/Java for production and Rust for side projects, he blends deep technical implementation skills with cross-functional product collaboration. He’s a lifelong learner who enjoys automating messy operational problems—evidenced by contributions to tooling like macOS cleanup scripts and robust ETL frameworks.
11 years of coding experience
8 years of employment as a software developer
University of Georgia
BE, Computer Science and Engineering, BE, Computer Science and Engineering at Shri Ramdeobaba Kamla Nehru Engineering College, Katol Road
Contributions:5 commits, 2 PRs, 1 comment in 1 day
Contributions summary:Rajat focused on enhancing the functionality of a macOS cleanup script. Their contributions included implementing features to delete various cache directories, specifically for Firefox and Chrome, and addressing issues related to file path handling and directory size calculations. The user demonstrated expertise in scripting using bash by adding support for more cache cleaning and improving existing cleanup logic. This indicates a focus on automating system maintenance tasks.
Contributions:26 pushes, 1 branch in 5 years 7 months
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