Freelance
Highlights
Designed and developed core backend systems to streamline Order Management features, including lead capture, order creation, invoice generation, order tracking, and payment management, using Node.js and PostgreSQL.
Implemented Rider Punch-In/Out functionality to log work hours and track delivery history.
Automated financial workflows, including multi-stage payment tracking, payment reminders, and dynamic order price calculations with discount application logic.
Ensured smooth API integration, optimized database queries, and maintained scalable backend architecture.
App dev Internship
Highlights
Attended a 6-week internship focusing App/Web Development. Used my React Native skills to build the 'Kamdhanda: Job Search App,' providing a helpful tool for people looking for jobs.
Web Developer Lead
Highlights
Collaborated with team members and other teams to design, develop, and maintain the chapter's website.
Utilized HTML, JS, and CSS to create impactful and visually appealing website.
Supported the team in implementing new features, optimizing website performance, and ensuring functionality.
Data Structures & WebD Intern
Highlights
Interned at PepCoding Education Private LTD., gaining hands-on experience in Data Structures and Full Stack Web Development.
Worked closely with the website team of PepCoding, collaborating on various aspects of web development and contributing to The enhancement of the NADOS website.
Mentor
Summary
Provided guidance and support to 10th and 12th-grade girls, assisting them with their studies and career choices. Through one-on-one sessions and workshops, and helped them identify their strengths and make informed decisions about their future paths. My role focused on nurturing their academic growth and empowering them to pursue fulfilling careers.
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Bachelor of Technology
Electronics and Communication Engineering-Artificial Intelligence
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Intermediate
PCM
Grade: 83.4%
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Matriculation
Grade: 79.4%
Solved 300+ coding problems on LeetCode.
Summary
Developed a deep learning model for skin cancer classification, achieving a 97.77% test accuracy. Built as a minor project focused on accurate diagnosis through advanced classification techniques. Accepted for presentation at the 4th International Conference on Advanced Computing and Intelligent Technologies (ICACIT 2024).