Jeffrey Tang
Cedar Park, Texas, United States
Summary
🤩
Rockstar🎓
Top SchoolJeffrey Tang is an IT generalist with over a decade of hands-on experience supporting organizations across Texas, from manufacturing to education and municipal projects. He’s skilled in Microsoft 365 and Windows Server administration, Azure AD/Entra, Active Directory, Group Policy, and has been part of cloud migration projects and multi-customer service delivery using ConnectWise. Comfortable troubleshooting hardware, networks, and end-user issues in both remote and on-site settings, he progressed from Level 1 to Level 2 support by taking on infrastructure maintenance and vendor coordination. Jeffrey also contributes to open-source ML tooling by testing and hardening parts of the Ludwig AI project, showing an appetite for learning beyond traditional IT ops. Based in Cedar Park, TX, he seeks roles that offer growth into deeper cloud or automation engineering while continuing to keep teams productive.
12 years of coding experience
1 year of employment as a software developer
Bachelor of Arts (B.A.), International Political Economy, Bachelor of Arts (B.A.), International Political Economy at The University of Texas at Dallas
English, Chinese, Spanish
Github Skills (9)
unit-testing10
machine-learning10
python10
ml10
testing10
data-science9
deep-learning9
pytorch9
natural-language-processing8
Programming languages (4)
Github contributions (5)
ludwig-ai/ludwig
Sep 2021 - Nov 2022
Low-code framework for building custom LLMs, neural networks, and other AI models
Role in this project:
ML Engineer & QA Engineer / Test Automation Engineer
ML Engineer & QA Engineer / Test Automation EngineerContributions:57 reviews, 31 commits, 48 PRs in 1 year 2 months
Contributions summary:Jeffrey primarily contributed to the testing and quality assurance of the Ludwig AI framework. Their work included implementing and testing new features, specifically focusing on shape-based unit tests for category and set input features. They also addressed a typo in a category feature shape test. Furthermore, they made modifications to ensure the robustness and functionality of the codebase, exemplified by fixing an issue related to zero-length image byte buffers.
ailow-codeneural-networkdeep-learningmachine-learning
jeffreyftang/ray
Dec 2021 - Feb 2022
An open source framework that provides a simple, universal API for building distributed applications. Ray is packaged with RLlib, a scalable reinforcement learning library, and Tune, a scalable hyperparameter tuning library.
Contributions:2 pushes, 1 branch in 1 month
apirayscalabledistributed-applicationshyperparameter
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