Machine Learning Engineer
CurrentDeveloped a comprehensive system designed to detect identity fraud.- Created synthetic datasets for training a deep learning model.- Implemented various features, including optical character recognition (OCR) matching, template matching, selfie comparison, behavioral randomness detection, DOVID detection, and integrity checks for identification documents.- Prepared a FastAPI script for integrating the system into a mobile application.- To enhance system usability and ensure continuous operation, Dockerization was employed to automate the necessary setup, downloads, and file transfers.- The system’s primary advantage was its ability to generalize methods for integrating with identification documents from various countries.Conducted research and testing in the field of Face Anti-Spoofing (FAS). Contributed to data preprocessing stages for FAS datasets.Currently, I am engaged in developing a system capable of detecting deepfake videos and credit scoring.