Jun-hao Wan
Cloud Infrastructure Engineer at 自由職業
New Taipei, Taiwan
Summary
🤩
Rockstar🎓
Top SchoolJun-hao Wan is a Cloud Infrastructure Engineer based in New Taipei, Taiwan with six years of experience building reliable CI/CD pipelines and Kubernetes-powered deployments. He has helped optimize production-grade Azure AKS integrations and observability stacks while at Getac and now applies that expertise at Trend Micro. As an active open-source contributor to the Ray ecosystem (notably KubeRay and the ray-project CI/CD), he focuses on code quality, linting, and improving cluster observability through Grafana enhancements. His background spans hands-on automation—from Android and web CI/CD to Kubernetes service hardening—combined with a practical emphasis on deployment stability. Known for squeezing operational risk out of complex systems, he blends developer ergonomics with production observability.
6 years of coding experience
2 years of employment as a software developer
Bachelor's degree, Department of Computer Science & Information Engineering, Bachelor's degree, Department of Computer Science & Information Engineering at 高雄科技大學(National Kaohsiung University of Science and Technology)
Github Skills (12)
python10
devops10
cicd10
kubernetes-pods9
grafana9
kubernetes9
automations8
automation8
data-science5
deep-learning5
deeplearning-ai5
machine-learning5
Programming languages (14)
Github contributions (5)
ray-project/ray
Sep 2024 - Mar 2025
Ray is an AI compute engine. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.
Role in this project:
DevOps Engineer
DevOps EngineerContributions:36 reviews, 24 PRs, 70 comments in 6 months
Contributions summary:Jun-hao primarily contributed to improving the continuous integration and continuous deployment (CI/CD) processes. Their work included fixing flake8 rule violations, adding JSON checks to pre-commit hooks, and integrating a "Cluster" variable into Grafana dashboards to enhance monitoring. These changes suggest an emphasis on code quality, build stability, and improved observability within the Ray project's Kubernetes environment. Furthermore, the user refactored code to address various linting issues.
pythonconsistsruntimetensorflowserving
win5923/ray
Sep 2024 - Apr 2025
Ray is a unified framework for scaling AI and Python applications. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.
Contributions:2 PRs, 156 pushes, 41 branches in 6 months
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