Haotian An
Software Engineer II at Amazon Web Services (AWS)
New York, New York, United States
Summary
🤩
Rockstar🎓
Top SchoolHaotian An is a Software Engineer II at AWS with seven years of experience building scalable cloud and ML tooling, currently contributing to professional services and previously working on EC2 VPC dataplane. He has hands-on experience integrating machine learning into production workflows—contributing notable examples and SDK enhancements to Amazon SageMaker, including NLP churn prediction notebooks and JumpStart model support. Trained at Tsinghua and Johns Hopkins (MS in Information Security), he combines systems-level engineering with practical ML model deployment and code quality focus. Colleagues describe him as a pragmatic implementer who moves prototype ML work into robust SDKs and examples that help other engineers adopt SageMaker features.
7 years of coding experience
3 years of employment as a software developer
The University of Hong Kong (HKU)Master of Science - MS Computer and Information Systems Security/Information Assurance, Master of Science - MS Computer and Information Systems Security/Information Assurance at Johns Hopkins Whiting School of EngineeringBachelor's degree Electrical and Electronics Engineering, Bachelor's degree Electrical and Electronics Engineering at Tsinghua University
Chinese, English
Github Skills (14)
amazon-sagemaker10
jupyter-notebook10
machine-learning10
deep-learning10
tensorflow10
aws10
python10
nlp9
unit-testing9
pytorch8
scikit-learn8
scikit8
ml-deployment7
continuous-deployment7
Programming languages (3)
Github contributions (5)
aws/sagemaker-python-sdk
May 2023 - Jan 2025
A library for training and deploying machine learning models on Amazon SageMaker
Role in this project:
ML Engineer
ML EngineerContributions:109 reviews, 37 PRs, 98 comments in 1 year 8 months
Contributions summary:Haotian primarily contributed to the development and enhancement of the Amazon SageMaker Python SDK, focusing on the integration of JumpStart proprietary models. Their work involved adding support for proprietary models, including parsing specifications, creating a dedicated JumpStartModel class, and implementing unittests. They also addressed code quality concerns, including linting, docstyle issues, and refactoring for performance.
pytorchsagemakerdeployingmxnetpython
aws/amazon-sagemaker-examples
Jan 2023 - Jan 2023
Example 📓 Jupyter notebooks that demonstrate how to build, train, and deploy machine learning models using 🧠Amazon SageMaker.
Role in this project:ML Engineer
ML EngineerContributions:1 review, 6 commits, 7 PRs in 18 days
Contributions summary:Haotian primarily contributed to the development of machine learning examples within the Amazon SageMaker ecosystem. Their work includes implementing and integrating a churn prediction model with text data, as evidenced by the addition of new notebooks, code modifications within Python files, and the use of Sentence Transformers for text processing. These changes suggest a focus on developing and integrating machine learning models into the example repository, with specific focus on natural language processing and model training.
pythonjupyter-notebooktrainingawssagemaker
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