Kamalesh Palanisamy
New York, New York, United States
Summary
🤩
Rockstar🎓
Top SchoolKamalesh Palanisamy is an experienced software engineer with 8 years of hands-on experience in back-end systems and MLOps, currently based in New York. He contributes to notable open-source projects like ZenML—improving pipeline reliability and UX—and Quickwit, where he enhanced ingest API controls and modernized database interactions for a cloud-native search engine. Comfortable across databases, API design, and deployment concerns, he focuses on practical improvements such as clearer error handling, tests, and robust migrations that reduce operational friction. His GitHub and Google Scholar presence signals both engineering rigor and an interest in research-informed solutions. Known for a pragmatic, detail-oriented approach, he jokes that he’s “trying to find the right hyperparameters for my life,” hinting at a data-driven mindset applied beyond code.
8 years of coding experience
The University of Texas at Dallas
English, Tamil, Hindi, Arabic
Stackoverflow
3reputation
26reached
0answers
1question
Github Skills (24)
postgresql10
back-end-development10
python10
databases10
workflow-engine10
mlops10
pipeline10
rust10
database10
testing9
search-engine9
cloud-native9
management8
developer-tools8
sql8
Programming languages (9)
Github contributions (5)
quickwit-oss/quickwit
Aug 2023 - Jan 2024
Cloud-native search engine for observability. An open-source alternative to Datadog, Elasticsearch, Loki, and Tempo.
Role in this project:
Back-end Developer
Back-end DeveloperContributions:15 reviews, 7 PRs, 31 comments in 5 months
Contributions summary:Kamalesh contributed to the backend functionality of the Quickwit search engine. Their work involved implementing and configuring content limits for the ingest API, modifying shard table entries to differentiate between local and remote shards, and updating SQL statements to utilize sea_query for database interactions. Additionally, the user added database migrations for a shards table in the postgres metastore. These changes enhanced the API's control, improved data management, and modernized the database interaction layer.
analyticsrustlog-analyticslog-managementlogging
zenml-io/zenml
Dec 2021 - Jan 2022
ZenML 🙏: The bridge between ML and Ops. https://zenml.io.
Role in this project:MLOps Engineer
MLOps EngineerContributions:11 reviews, 9 commits, 2 PRs in 7 days
Contributions summary:Kamalesh primarily focused on improving the integration and functionality of the ZenML pipeline, specifically by addressing installation issues related to system requirements. They modified error messages for clarity and added error handling for specific pipeline scenarios. Furthermore, the user added tests to ensure the pipeline's robustness and maintainability. The contributions demonstrate a focus on improving the overall reliability and user experience of the ZenML framework.
devops-toolspythonproduction-readytensorflowproduction
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