Hi, I'm Benjamin Dong

Led the design and deployment of enterprise-scale agentic AI systems using Python, LangGraph, LangChain, and Azure OpenAI, delivering intelligent automation solutions that reduced case triage time by 42% and improved workflow completion rates by 35%. Built and operated cloud-native AI platforms on AWS and Azure, integrating machine learning, vector search, observability, and secure APIs to support over 1 million monthly requests while achieving 99.9% availability and reducing operational costs by 24%.

Developed and deployed machine learning, NLP, and computer vision solutions using Python, TensorFlow, PyTorch, Scikit-learn, spaCy, and OpenCV, improving classification accuracy, defect detection performance, and document processing efficiency across enterprise workflows. Optimized large-scale data processing and model training pipelines with Spark and Hadoop while establishing reproducible MLOps practices, accelerating model deployment and driving adoption of AI-driven analytics across multiple business units.

Developed predictive analytics and machine learning solutions using Python, R, and Scikit-learn, improving forecasting accuracy and accelerating data-driven decision-making for business and operations teams. Collaborated with cross-functional engineering and data science teams to evaluate, deploy, and operationalize ML models, enhancing project delivery efficiency and increasing on-time execution of AI initiatives.

Designed and maintained scalable backend services and REST APIs using Python and Java, improving application performance and enabling reliable access to large-scale analytics data. Enhanced software delivery and operational efficiency through CI/CD automation, big data processing with Hadoop and Spark, and proactive production support that significantly reduced deployment issues and recurring incidents.