● Developed machine learning models that ran 6000+ customer images per day in real-time to alert users regarding improper face uploads at the front end, increasing photo acceptance rate from 80% to 90%. ● Created a machine learning model using XGBoost to predict and identify churned users in advance having 93% recall, and led marketing plan campaigns. ● Devised CNN classifier for document recognition across 11 classes, achieving 97% precision and 92% accuracy. ● Engineered scalable ML models that handle 20000+ calls per day from the backend and frontend, automating the KYC verification process and streamlining the onboarding journey for users. ● Designed a K-Means clustering approach to segment 1.1M users according to investment habits in Mutual Funds. ● Initiated statistics-based verification of account statements for Income and Bank Proof, automating 85% of account statement pdf uploads.

● Designed ML models to extract Period and Names from Account Statements with 95% precision. ● Built ML model to perform similarity analysis using Random Forest Classifier with 98% precision. ● Deployed models as Rest API using FastAPI for scaling.

● Performed literature survey to identify gripper selection and pose estimation for robotic grippers in assembly lines. ● Created an AI-based system based on computer vision for job recognition to estimate object pose and coordinates with 100% accuracy to grasp on the test set. ● Published Real Time Deep Learning-Based Image Processing for Pose Estimation and Object Localization in Autonomous Robot Applications, The International Journal of Advanced Manufacturing Technology, Springer.