PALADUGU VARIJA

About

Full Stack Engineer focused on web development and AI applications. Built projects in computer vision, career chatbots, and interactive learning platforms using modern JavaScript frameworks, Python, and deep learning tools.

Education

• 2027
  B.Tech

  Gokaraju Lailavathi Engineering College

  Computer Science and Engineering (CSE) · Sep 2023 – 2027

  Computer Science and Engineering (CSE) · Sep 2023 – 2027
• 2023 CJ
  Senior Secondary

  Sri Chaitanya Junior College

  Jun 2021 – May 2023

  Jun 2021 – May 2023

Skills

Tools / apps / platforms

Projects

• LearnLingua — Gamified AI Learning Platform
  React.js, Git

  Developed the React.js frontend module for transforming PDFs, audio, and video into interactive gamified learning content. Integrated the content processing pipeline with the frontend to dynamically display generated learning modules. Designed interactive learning components to improve user engagement and make generated content easier to consume. Worked with the team to integrate frontend functionality with the underlying AI content-generation workflow. Led the team from concept to a working prototype within a 24-hour hackathon, coordinating development and managing the project using Git.

• ASHA AI — Intelligent Career Chatbot Platform
  Next.js, Firebase, JWT, Markdown

  Developed a Next.js interface for an AI-powered career platform integrating chatbot, job search, and event discovery features. Built a dynamic Markdown-based chat interface for presenting structured AI-generated responses. Created and organized CSV/JSON datasets to support backend semantic search and career-related information retrieval. Integrated Firebase Authentication and JWT-based authentication flows with the frontend for secure user access. Developed reusable frontend components to support an interactive and responsive career assistance experience.

• Scene to Shop — AI Based Visual Shopping Assistant
  React.js, Python, YOLOv8

  Built a React.js web interface for image upload and real-time product detection with bounding boxes and object labels. Integrated the frontend with a Python backend API for YOLOv8 inference and product category mapping. Implemented image processing and API communication to connect the detection pipeline with the web interface. Tested and debugged the application across devices, achieving approximately 85% detection accuracy on common product categories. Designed the application to provide an interactive visual shopping experience by identifying products directly from uploaded images.

• PD Predict — Multimodal Parkinson's Disease Detection
  Streamlit, TensorFlow, OpenCV, CNN

  Developed a Streamlit-based application for Parkinson’s disease prediction and visualization of detection results. Built and integrated a CNN-based deep learning model using TensorFlow and OpenCV for analyzing input data and generating predictions. Designed an interactive interface to display prediction outputs, confidence information, and model results in a user-friendly format. Implemented image preprocessing and input handling with OpenCV to prepare data for model inference. Integrated the trained model with the Streamlit frontend to provide an end-to-end prediction workflow.

Courses & certifications

• Google Skill Boost · Google · 2025
• Programming in Java · NPTEL · 2025
• The Joy of Computing using Python · NPTEL · 2024