We designed, developed, and deployed a High-Frequency Trading (HFT) System and a few trading strategies. The system comprises numerous backend services and frontend applications, capable of handling thousands of prices in real-time. The technology stack is diverse, featuring Python, Rust, and Redis for backend development and Angular for frontend development. The infrastructure was built on Amazon Web Services (AWS).

Design and build a large infrastructure (fully Infrastructure as Code) for document digitization, OCR, and Content Analysis. The platform was able to handle tens of millions of documents.

Development of a churn model for Medicaid and Medicare, and its infrastructure in Azure Machine Learning.

Design and development of a fully serverless machine learning platform for super-fast and simple model deployment with massive scaling capabilities. The infrastructure was built on Amazon Web Services (AWS).

Rearchitecting and replatforming of several monolithic applications into microservices and deployment in Google Kubernetes Engine (GKE).

Research & Development of Expert-Augmented Machine Learning (EAML), a methodology and algorithm to build high-precision and trustworthy machine learning models. Paper published in Proceedings of the National Academy of Sciences (PNAS).

Created a framework for Business Process Optimization (BPO) which allowed significant time savings and quality gains for many business processes. Led the reengineering and automation of many business processes.