Ning Jiang
安全架构师(Cyber Security Architect) at 中国联通 (ChinaUnicom)
Haidian District, Beijing, China
Summary
🤩
Rockstar🎓
Top SchoolNing Jiang is a cyber security architect based in Haidian, Beijing with over 10 years building carrier-grade security solutions for operators and enterprises. Currently at ChinaUnicom, he designs and drives cloud, network and endpoint security offerings tailored to telecom scenarios, drawing on extensive pre-sales, delivery and product leadership at Huawei, Orange and other vendors. He blends deep technical roots—from early systems and automation engineering to hands-on solution architecture—with product and go-to-market experience as a former technical director and startup partner. Ning also contributes to prominent open-source ML and agentic-AI projects (notably Microsoft’s SynapseML and Autogen), improving AutoML scalability with Spark and integrating end-to-end ML lifecycle tooling. His uncommon mix of carrier networking experience, large-scale security program delivery and practical data/ML engineering enables him to translate advanced research and open-source work into deployable, revenue-generating security services.
10 years of coding experience
20 years of employment as a software developer
Bachelor of Science (BS), Automation engineering, Good, Bachelor of Science (BS), Automation engineering, Good at Southwest Jiaotong University
English
Github Skills (18)
apache-spark10
spark10
python10
data-science10
machine-learning10
pyspark10
mlflow10
automl10
chromadb9
microsoft-azure9
ai9
azure9
ml-deployment8
notebook8
continuous-deployment8
Programming languages (7)
Github contributions (5)
microsoft/autogen
Sep 2023 - Feb 2025
A programming framework for agentic AI 🤖 PyPi: autogen-agentchat Discord: https://aka.ms/autogen-discord Office Hour: https://aka.ms/autogen-officehour
Role in this project:
Backend Developer & DevOps Engineer
Backend Developer & DevOps EngineerContributions:2 releases, 322 reviews, 128 PRs in 1 year 5 months
Contributions summary:Ning primarily contributed to enhancing the FLAML library, focusing on integrating Spark for parallel training and expanding the functionality of the AutoML system. They added support for Spark DataFrames as input datasets and Spark models as estimators, thereby improving the library's scalability and performance. The user also addressed various bugs and made improvements related to Spark integration and improved the overall robustness of the system, including modifications to core components, and improving the automated testing procedures. Moreover, the user demonstrated a deep understanding of the AutoGen library and contributed to its RetrieveChat module.
agenticagentic-agiagentsaiautogen
microsoft/SynapseML
Aug 2022 - Oct 2022
Simple and Distributed Machine Learning
Role in this project:Data Scientist
Data ScientistContributions:2 reviews, 7 commits, 7 PRs in 2 months
Contributions summary:Ning's commits primarily focus on adding and modifying notebooks for various AI sample applications within the `microsoft/synapseml` repository. These include examples for book recommendations, text classification, fraud detection, and uplift modeling. The changes involved incorporating MLFlow for logging and loading models, as well as simplifying data downloading processes, indicating a focus on the end-to-end machine learning lifecycle.
fairness-mlpythoncaffe2model-deploymentcognitive-services
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