Resume
Full-Stack Developer & UI/UX Designer with expertise in building scalable web applications and creating intuitive user experiences.
Summary
Jeevan P Naik
Computer Science student (8.7 CGPA) with expertise in full-stack development (MERN Stack) and UI/UX design. Developed multiple web applications focusing on user-centric solutions and scalable architecture.
- Moodkeri, Hegde, Kumta, Karnataka
- +91-8618621106
- naikjeevan66@gmail.com
Education
BE - Computer Science & Engineering
2021 - 2025
Mangalore Institute of Technology & Engineering, Karnataka
CGPA: 8.7
Pre-University Course (12th)
2019 - 2021
Saraswati PU College, Kumta
Percentage: 91.5%
Secondary Education (10th)
2017 - 2019
C V S K High School, Kumta
Percentage: 91.36%
Skills
- Languages: Java, Python
- Web: HTML, CSS, JavaScript, React.js
- Backend: Node.js, Express.js
- Database: SQL, MongoDB, Firebase
- Tools: Visual Studio, Eclipse, Figma, Framer, Blender, Canva, Da Vinci Resolve
- Technologies: Web Development, UI/UX Design, Web Design
Internship
Full Stack Developer (MERN) Intern
Inventeron Technologies | Feb 2025 – June 2025
Project: Language Learning Platform
Technologies: React, Bootstrap, Web Speech API, Node.js, Express.js, MongoDB, OpenAI, Google OAuth 2.0, JWT
- Built an AI-powered MERN stack application for immersive, gamified language learning integrating speech recognition, flashcards, AI chatbot interaction, structured lessons with quizzes, and secure authentication.
- Features include voice pronunciation practice, gamification elements, and personalized progress tracking.
Full-Stack Developer Intern
Tekkybench Technologies | Apr–May 2024
- Developed an event management platform using PHP, MySQL, HTML, CSS, and JavaScript.
- Integrated Razorpay payment gateway, handling 500+ transactions securely.
- Implemented real-time analytics with MySQL for attendee tracking and insights.
Projects
Smart Attendance System using Face Recognition
April 2025
Technologies: Python, Flask, OpenCV, PHP, MySQL
- Developed an automated attendance system leveraging face recognition for real-time attendance marking.
- Implemented a Flask API for backend processing and integrated OpenCV for face detection.
- Created a PHP web dashboard for managing and visualizing attendance data with reporting and analytics features.
SoilSense - AI-Powered Crop Recommendation System
June 2023 – Present
- Developed an AI-driven crop recommendation system using soil image analysis.
- Integrated real-time weather data for precise crop suggestions.
- Implemented multi-language support and live camera functionality for ease of use.
Tuberculosis Detection System
Jan–Apr 2024
- Designed a web-based system for detecting Tuberculosis using chest X-ray images.
- Leveraged pre-trained CNN models with Grad-CAM for interpretability.
- Provided real-time feedback with confidence scores and highlighted affected regions.