I'm Veeresh Kande.

At the University of Illinois Urbana-Champaign, I gained a strong foundation in Data Science and learned how machine learning technology started and evolved into Generative AI. I got hands-on experience integrating Generative AI into software applications. I also learned how to work with large datasets and how to apply this knowledge to real-world problems.

At BITS Pilani, I gained a strong fundamental understanding of civil engineering and had the opportunity to explore courses in data science. I worked on projects that applied AI in civil engineering, such as using machine learning for Structural Health Monitoring and analyzing precipitation data in Hyderabad to devise optimal strategies for preventing floods and pollution of rainwater.

At Gameberry Labs, I focused on developing Android and iOS mobile game applications, ensuring optimal performance and scalability. I implemented new features, optimized existing ones, and enhanced internal tools using React to improve team efficiency. My work involved using Golang for backend development and C++ with the Cocos Game Engine for the Game UI.

At OYO, I was responsible for developing new features and maintaining the backend of OYO’s Pricing and Inventory system, which is developed using Progress ABL. I worked on migrating the existing monolithic codebase to a Spring Boot microservices architecture. Additionally, I created Grafana dashboards and alerts to monitor service health and developed Jenkins pipelines to automate the code deployment process. I also migrated the backend application from PHP to Azure and enhanced the existing machine learning model to improve hotel inventory management.

Developed experimental full stack web applications for a state government project using the MERN stack (MongoDB, Express, React, Node.js). These applications facilitated the creation and tracking of road cutting requests, improving project management and efficiency.

Developed a dynamic dashboard for the Tamil Nadu eGovernance customer service team using RShiny and R. The dashboard featured current unresolved calls and options to display top counts for services and areas receiving the most calls. This tool enabled the team to analyze data effectively and improve customer awareness of government digital services.