Siddhant Nitin Patil

Master's

Computer Science

Humancloud

Full Stack Software Engineer

- Launched innovative solutions with a service-oriented architecture, boosting software development efficiency - Delivered seamless, highly responsive UI (user interfaces) and high-quality UX for mobile applications, working as a shadow on client projects, enhancing efficiency by 40%, and providing inclusive documentation for future reference - Engineered the integration of Google Map APIs into the interface, enabling precise file location services that improved user experience metrics by 40% and increased user engagement by 25% in the navigation app sector - Advanced PDF rendering techniques with integrated editor options for digital verification using React-PDF, resulting in a 25% increase in document verification efficiency, streamlining the verification process significantly - Transitioned codebase from Angular to React, creating 80% reusable assets and adding features. Emphasized customer experience and responsive design throughout the process - Designed efficient APIs in Express.js using Postman, which streamlined API execution and microservices advancement, significantly reducing API response time by 30% and improving application performance - Projects - Responsive ECommerce Platform System - TypeScript, NodeJS, React, PostgreSQL, Git - Replicated the available e-commerce platform using modern frameworks and web technologies, improved scalability and performance, and used headless CMS, anticipated the user experience boost by 55 - Implemented a secure user authentication system with registration, login, and password recovery functionalities using Node.js and Express, enhancing security and reducing unauthorized access attempts by 40 - Integrated a robust product management system, allowing administrators to efficiently add, edit, and delete product listings and manage inventory levels, leading to a 30% reduction in administrative overhead - Smart Traffic Control System - Python, TensorFlow, Keras, OpenCV, PyTorch - Integrated real-time data collection and analysis from sensors and cameras, enabling accurate traffic density assessment and decision-making, decreasing traffic accidents by 20 - Implemented machine learning algorithms for traffic pattern prediction, improving traffic flow efficiency by 25% and reducing average travel time by 15% for commuters - Conducted extensive testing and validation, achieving a 95% accuracy rate in traffic predictions and ensuring robust performance under various environmental conditions