Xiaodong Yan
Software Engineer at Caruso
Chengdu City, Sichuan, New Zealand
Summary
👤
Senior🎓
Top SchoolXiaodong Yan is a software engineer who combines 14+ years of automation and CI/CD experience with recent work building production-grade AI and agent-driven DevOps solutions. He has repeatedly taken projects from 0 to 1—designing CI/CD and test automation frameworks, leading small DevOps teams, and embedding RAG-based intelligence into operational workflows. Notable work includes DevOpsGPT and contributions to a beginner-friendly deep learning repo where he implemented perceptron and iris classification models, reflecting both practical ML chops and system-level engineering. His background spans enterprises (ANZ, EPAM, Adobe) and cloud/agent engineering (Azure DevOps, Kubernetes, LangChain, AutoGPT), enabling teams to run adaptive, self-healing test pipelines. A recent Master's focus on machine learning and cloud computing complements hands-on experience with prompt engineering, AI evaluation (DeepEval, Phoenix), and parallelized execution to cut test times. Based in Chengdu with ties to New Zealand, he blends pragmatic automation expertise with a creative push toward autonomous AIOps.
11 years of coding experience
10 years of employment as a software developer
bachelor, electronic technology, bachelor, electronic technology at Yanshan UniversityMaster's degree, Information Technology, Master's degree, Information Technology at The University of Waikato
Stackoverflow
1reputation
0reached
0answers
0questions
Github Skills (8)
perceptron10
machine-learning10
deep-learning10
python10
pandas9
data-science8
scikit4
scikit-learn4
Programming languages (4)
Github contributions (5)
hanbt/learn_dl
Oct 2017 - Nov 2018
Deep learning algorithms source code for beginners
Role in this project:
ML Engineer
ML EngineerContributions:11 commits, 1 PR, 2 comments in 1 year 1 month
Contributions summary:Xiaodong primarily contributed to the development of deep learning algorithms within the `learn_dl` repository, implementing a perceptron model in Python. Subsequent commits involved extending this foundational work by creating a class for classifying iris flowers. The user demonstrated practical application of the perceptron model, including training and prediction, and integrating it with a dataset, highlighting a focus on model implementation and evaluation.
pythondeep-learningfor-beginnersmachine-learninglearning-algorithms
PlayPython/homework
Mar 2018 - May 2019
Contributions:56 commits, 35 pushes, 2 branches in 1 year 1 month
practicingpythonpython3
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