About
Highly accomplished AI/Machine Learning Engineer with 7+ years of experience specializing in developing, deploying, and optimizing advanced machine learning models across diverse industries. Proven ability to translate complex business problems into scalable AI solutions, driving significant improvements in operational efficiency, revenue growth, and data-driven decision-making. Eager to leverage deep expertise in deep learning, MLOps, and cloud platforms to innovate and lead impactful AI initiatives.
Work
San Francisco, CA, US
→
Summary
Led end-to-end development and deployment of scalable AI solutions, optimizing model performance and integrating MLOps practices to drive significant business value.
Highlights
Architected and deployed a real-time fraud detection system using Graph Neural Networks, reducing fraudulent transactions by 18% and saving the client over $2.5M annually.
Developed and productionized a recommendation engine for an e-commerce platform, increasing user engagement by 22% and boosting conversion rates by 15% within six months.
Optimized existing deep learning models for inference speed, achieving a 30% reduction in latency and cutting cloud infrastructure costs by 10% through efficient resource utilization.
Mentored a team of 3 junior ML engineers, establishing best practices for model development, code reviews, and MLOps pipelines using Kubeflow and Azure ML.
Designed and implemented A/B testing frameworks for ML models, ensuring robust evaluation and iterative improvement of deployed solutions based on key business metrics.
Seattle, WA, US
→
Summary
Designed, developed, and maintained machine learning models and data pipelines to support predictive analytics and automation initiatives across various departments.
Highlights
Developed a predictive maintenance model for industrial machinery using time-series data, forecasting equipment failures with 92% accuracy and reducing unplanned downtime by 25%.
Implemented robust ETL pipelines using Apache Spark and Airflow to process terabytes of raw data, improving data availability for ML training by 40% and reducing processing time by 3 hours daily.
Collaborated with product teams to integrate ML-powered features into core applications, enhancing user experience and contributing to a 10% increase in product adoption.
Researched and experimented with various machine learning algorithms (e.g., XGBoost, Random Forest, SVM) to identify optimal solutions for classification and regression tasks, improving model precision by up to 5%.
Containerized ML models using Docker and deployed them onto AWS SageMaker, streamlining the deployment process and enabling rapid iteration of model versions.
Central City, IL, US
→
Summary
Conducted research on novel deep learning architectures for natural language processing, contributing to academic publications and advancing state-of-the-art methods.
Highlights
Developed a novel neural network architecture for sentiment analysis on social media data, achieving a 3% improvement in F1-score over baseline models.
Authored and co-authored 2 peer-reviewed publications in top-tier AI conferences (e.g., ACL, EMNLP), presenting findings to the academic community.
Implemented and evaluated various natural language processing techniques, including word embeddings, recurrent neural networks, and attention mechanisms, for text classification tasks.
Managed and preprocessed large-scale textual datasets, ensuring data quality and readiness for model training and evaluation.
Education
→
Ph.D.
Computer Science (Specialization in Artificial Intelligence)
Grade: Dissertation: 'Deep Learning for Low-Resource Natural Language Understanding'
Courses
Advanced Machine Learning
Deep Learning Architectures
Natural Language Processing
Reinforcement Learning
Computer Vision
Languages
English
Mandarin Chinese
Skills
Programming Languages
Python, SQL, Java, R, Bash.
Machine Learning Frameworks
TensorFlow, Keras, PyTorch, Scikit-learn, XGBoost, LightGBM, Hugging Face Transformers.
Deep Learning
CNNs, RNNs, LSTMs, Transformers, GANs, Reinforcement Learning, NLP, Computer Vision.
Cloud Platforms
AWS (SageMaker, EC2, S3, Lambda, EKS), Azure (Azure ML, AKS, Data Lake), GCP (AI Platform, BigQuery, GKE).
MLOps & Deployment
Docker, Kubernetes, MLflow, Airflow, CI/CD, FastAPI, Streamlit, API Development.
Data Engineering & Databases
Spark, Pandas, NumPy, SQL, NoSQL, PostgreSQL, MongoDB, Data Warehousing.
Tools & Methodologies
Git, Jupyter, VS Code, Agile, Scrum, Experiment Tracking, Model Monitoring.
Interests
Technology
Quantum Computing, Robotics, Generative AI.
Hobbies
Hiking, Photography, Chess.