● Engineered a sophisticated bot that autonomously manages and records meetings across multiple platforms including Microsoft Teams, Google Meet, and Zoom, significantly enhancing meeting efficiency for ABR and its clients.● Architected and implemented multithreading, robust API integration with ABR’s API and Virtual Audio Cables, enabling the bot to handle over 10 high-profile meetings simultaneously, reducing computational cost by 46%, during my internship.● Integrated an NLP feature to convert speech to text on recorded videos, enabling automatic transcription.

• Spearheaded the design and implementation of an AI-powered early detection system for Light Utility Vehicles (LUVs), reducing unexpected downtimes by 15% and extending vehicle longevity. Leveraged predictive analytics to proactively identify maintenance needs, ensuring optimal vehicle performance and reliability.• Innovated a path-finding algorithm using OpenCV to enhance the navigation capabilities of LUVs in complex agricultural terrains. This algorithm improved navigation safety, enabling vehicles to accurately assess and maneuver through tight spaces and obstacles, thereby minimizing the risk of accidents and damages, as an intern.

• Developed end-to-end intuitive, full-stack web applications, employing a modern stack of NodeJS, GraphQL, ExpressJS, React, and Redux. This experience taught me the importance of teamwork, clean code, and user-focused design, contributing to an enhanced user experience.• Assisted in integrating web application's database with AWS DynamoDB, a task that introduced me to the complexities of cloud databases and the critical role they play in scaling applications to meet growing user demands, during the internship.

• Led the development and deployment of cutting-edge ML models for cancer detection, achieving near-perfect precision and recall rates while maintaining model sizes under 100MB. This breakthrough contributed to early and accurate cancer diagnosis, potentially saving lives, all during my internship.• Directed the rapid creation and integration of forklift box and rust detection models in a warehouse setting, utilizing OpenCV and Intel D345 depth sensing technology. Successfully delivered the project within a one-week timeframe, enhancing operational efficiency by 35% and integrating the solution with GCP to leverage cloud capabilities and storage.