Sandeep Muralidharan

• Designed and implemented a scalable AI-based mobile application, implementing feature-based architecture with Kotlin, Jetpack compose and GCP improving UI efficiency by 20%
• Deployed a Python script to integrate the SportsMonk API, enabling dynamic fixture retrieval with filtering and pagination improving data sync speed by 60%.

• Engineered an Al-powered pet assistant, embedding a ChatGPT wrapper in FlutterFlow, enhancing response latency by 40%.
• Upskilled in React and Node.js within 1 month, developing fully responsive and functional frontend UIs that improved user experience and met tight deadlines, delivering projects 50% faster.

• Created responsive Flutter Web applications, ensuring cross-device compatibility and improving efficiency, reducing load times by 20%.
• Constructed high-performance mobile applications using FlutterFlow, leading to the successful deployment of multiple prototypes within a 2-month timeline, which helped onboard 5 more partners.

• Developed five Al mobile applications across multiple sectors, including multiple apps in the health sector
• Facilitated efficient data management with RESTful APIs, enabling seamless integration of user authentication flows, resulting in a 15% increase in user engagement within the initial month.

Built a machine learning model to predict housing prices, utilizing advanced techniques such as feature engineering and models like Random Forest and Ridge. Achieved an R² score of 92% by extracting and creating new features to enhance model performance.

Developed a 5-layer CNN to classify six types of vehicle damage including dent, scratch, and tire flat. Achieved 0.94 AUC and 0.72 precision using data augmentation, dropout, and class weighting for imbalance handling.