Ki Kwon is a Machine Learning Engineer at NAVER with nine years of experience building data-driven search and recommendation systems. Trained in both Computer Science and Business Administration at Seoul National University, Ki blends rigorous technical skills with product-minded analysis to turn user signals into actionable ML features. Past roles span data engineering, analytics, and project management across startups and enterprises, where Ki led NLP-driven review analysis, churn modeling, and recommender systems. An active backend contributor to the widely used JuiceFS project, Ki implemented secure HDFS integrations (Kerberos keytab support, nameservice handling and base64 keytabs) that strengthened scalable storage interoperability. Comfortable shipping production ML and engineering solutions, Ki brings a pragmatic focus on secure, maintainable infrastructure that bridges research and product needs.
9 years of coding experience
1 year of employment as a software developer
Bachelor's degree, Computer Science and Engineering, Bachelor's degree, Computer Science and Engineering at Seoul National University
JuiceFS is a distributed POSIX file system built on top of Redis and S3.
Role in this project:
Backend Engineer
Contributions:3 reviews, 8 PRs, 18 comments in 1 year 11 months
Contributions summary:Ki primarily focused on enhancing the JuiceFS's HDFS (Hadoop Distributed File System) integration. They implemented support for Kerberos keytab authentication, allowing secure access to HDFS. The user also added functionality to specify the HDFS path and enabled the use of nameservices in the HDFS configuration. Furthermore, they provided support for base64 encoded keytabs and addressed a compression-related issue.
JuiceFS is a distributed POSIX file system built on top of Redis and S3.
Contributions:24 pushes, 11 branches in 1 year 10 months
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