Rachit Chauhan
Staff Software Engineer - ML Platform
San Diego, California, United States
Summary
🤩
Rockstar🎓
Top SchoolRachit Chauhan is a Staff Software Engineer specializing in ML platform and data systems with 11 years of experience building scalable big-data, IoT, and model inference ecosystems. At Intuit he leads ML Ops initiatives and end-to-end model inferencing architecture while contributing to open-source projects like KServe, improving backend stability and deployment manifests for serverless ML on Kubernetes. He brings deep hands-on expertise across Hadoop distributions, Kafka/Flume/Sqoop data lakes, MapReduce, Oozie, and microservices, plus cloud-native CI/CD and lambda architectures. Known for thinking architecturally and solving cluster performance and operational issues, he pairs rigorous engineering (TDD, performance testing) with practical DevOps improvements. Based in San Diego, he learns constantly from peers and blends backend, infrastructure, and ML-serving know-how to move models from prototype to production.
10 years of coding experience
9 years of employment as a software developer
Bachelors of Technology, Computer Science, Bachelors of Technology, Computer Science at DAV Institute of Engineering and Technology
English, Hindi
Stackoverflow
1reputation
0reached
0answers
0questions
Github Skills (12)
kubernetes10
go10
kubernetes-pods10
cicd10
yaml10
knative9
dockers9
docker9
istio8
machine-learning6
tensorflow5
pytorch5
Programming languages (7)
Github contributions (5)
kserve/kserve
Oct 2022 - Jan 2023
Standardized Serverless ML Inference Platform on Kubernetes
Role in this project:
DevOps Engineer & Backend Developer
DevOps Engineer & Backend DeveloperContributions:102 reviews, 6 commits, 30 PRs in 2 months
Contributions summary:Rachit primarily contributed to the backend and infrastructure aspects of the project. Their work involved modifying the `cmd/router/main.go` file to incorporate header propagation and error handling. They also made changes to the deployment scripts and configurations, specifically focusing on version upgrades and manifest updates. These changes suggest a focus on improving the stability, deployment, and operational aspects of the KServe platform.
xgboostsklearnserverlessclient-goknative
rachitchauhan43/kserve
May 2022 - Jan 2024
Serverless Inferencing on Kubernetes
Contributions:20 PRs, 228 pushes, 99 branches in 1 year 8 months
serverlessmachine-learningmlopsk8skubernetes
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