Shubham Mahobia

I am Shubham Mahobia, a software engineer based in Bhopal, India. I am currently pursuing my B.Tech degree at SAGE University, with an expected graduation in 2025. Throughout my academic journey, I have maintained an impressive 8.49 CGPA, demonstrating my dedication and commitment to excellence.

As a software engineering intern at Mitt Arv, I have gained valuable experience in frontend and backend development. I have spearheaded the development of a Flutter application, resulting in a 20% increase in app responsiveness and a 15% decrease in load times. By implementing biometric authentication and a feedback system, I have contributed to a 25% increase in user engagement and a 30% rise in user satisfaction. Additionally, I have leveraged Node.js to develop RESTful APIs, achieving a 40% reduction in API response time and enabling seamless data communication between frontend and backend systems. My active participation in Agile methodology has led to a 10% increase in overall project efficiency and on-time delivery of features.

One of my notable projects is the development of a platform called Virtual Hospital. Using Flutter with Firebase authentication, I created a secure platform for doctors and patients to interact. By integrating appointment booking, end-to-end encrypted chatting, and video call capabilities, I have facilitated convenient and secure communication between doctors and patients. I have also implemented a diseases prediction AI model based on decision trees, utilizing NLP techniques to predict diseases based on user symptoms. The integration of Firebase Analytics has provided valuable insights into user behavior and engagement.

Another project I have worked on is RideEye, a bus tracking application. By developing a cross-platform mobile application using Flutter, I achieved a 30% reduction in development time compared to traditional native app development. With a scalable backend server built with Node.js, I improved data retrieval and processing response time by 50%. Managing a MongoDB database with over 100,000 records of real-time bus tracking data, I ensured high availability and reliability. By applying machine learning algorithms, I enhanced arrival prediction accuracy by 20%, resulting in a more efficient and reliable bus tracking system.