Raksha R Kademani
Full-Stack Developer + AI Integrations, Computer Vision, React, .NET, and PostgreSQL
Hi there
I’m Raksha, a Full-Stack Web Developer with 2+ years of industry experience and a current M.Sc. in Computer Science at IU International University. I am experienced in building scalable web applications, specializing in C#/.NET backend services, RESTful APIs, and modern frontend frameworks including React, Angular, and Blazor WebAssembly.
Currently working as an AI and Full Stack Developer Intern at Gaddr. I have a strong focus on clean code, usability, and modern web workflows, alongside hands-on experience with AI-assisted features and computer vision systems. If you’re interested in collaborating, feel free to drop me a line.
Send me a message
Education
M.Sc. in Computer Science
IU International University of Applied Sciences
B.E. in Computer Science
Visvesvaraya Technological University
Career Path
A chronicle of my professional evolution, from foundational web development to advanced AI systems and backend architecture.
AI & Full Stack Developer Intern
Gaddr
Developing features for an AI-powered recruitment platform, integrating backend services with frontend components. Managing JWT authentication, state management, and real-time updates using NestJS, TypeScript, PostgreSQL, and Redis.
Software Engineer
Tietoevry
Developed and maintained full-stack web applications using C#, .NET, Blazor WebAssembly, and MS SQL Server. Built RESTful APIs, implemented secure file handling, and wrote unit tests to improve application stability.
Skills & Tech
Featured Projects
AI Workflow Support Ticket System
Developed a full-stack analytics system with a React dashboard to visualize support ticket insights, powered by a multimodal NLP classification pipeline combining unstructured text and numerical metadata, complete with model evaluation and retraining workflows.
Smart Damage Detection
A smart AI-based computer vision system utilizing Convolutional Neural Networks (CNNs) to automatically identify and classify package damage in real-time. By integrating Explainable AI (XAI) techniques like GRAD-CAM and SHAP for transparent predictions, the system significantly reduces manual inspection bottlenecks, minimizes financial losses, and improves overall logistics efficiency.
Smart Dustbin Management System
Designed a data-driven system using K-Means clustering and Random Forest models to predict waste bin fill levels. Created an interactive Next.js dashboard to monitor capacities, optimizing collection routes by 30%.