1.Designed and implemented a high-performance machine learning solution to accurately predict Arrival Slippage, leveraging client trade data to enhance execution insights and decision-making using models like XGBoost using Scikit Learn.

2.Engineered a scalable Graph Neural Network-based pre-trade model to extract actionable client data patterns, supporting the trading desk with predictive insights for smarter order handling using frameworks like Pytorch and Pygeometric.

3.Built and operationalized a high-precision market volatility model, driving improved prediction accuracy across 54+ countries and enabling more informed global trading decisions using Scikit Learn.

4.Created a production-level beta model to forecast equity returns across 54+ international markets, informing investment decisions and enhancing risk-adjusted performance using Scikit Learn.

5.Designed and implemented an end-to-end pipeline to generate daily financial benchmark reports for order entities, aggregating and analyzing OMS/EMS transactions to support performance tracking and regulatory compliance using Python, Pandas, Numpy frameworks.

6.Leading a team of software engineers to deliver scalable, high-impact solutions, fostering collaboration and driving project success.

1. Developed Image Segmentation Algorithms using Deep learning, and used techniques like knowledge distillation and label smoothing to improve the performance of the model.

2.Posess Strong knowledge in the frameworks like Pytorch and Tensorflow.

3.Worked on Deploying Object Detection deep learning models like SSD,YOLO, using TENSORRT, CUDA, and other frameworks for higher throughputs and lower latencies.

4.Proficient in C++ and ROS, contributed to the sdk for the company and has a good expertise in the dealing with sensors and fusing information from sensors.

5.Worked on Object tracking and implemented tracking algorithms like Hungarian for maximum bipartite matching.