RaashidhRizvi

• 01

  Live2025 — 2026

  QuicMov.com

  Founder & Full Stack Engineer

  A multi-domain rental marketplace running in production with real users. Listings, bookings and real-time messaging sit on a Spring Boot backend, with an LLM assistant built on Azure AI Foundry handling conversational search and onboarding.

  • Deployed and actively maintaining a live production platform with real user traffic
  • Built an AI chatbot on Azure AI Foundry for conversational listing search, booking guidance and availability insights
  • Implemented secure authentication and authorization workflows with JWT and role-based access
  • Added real-time communication over WebSockets, and cut response times with Redis caching and pagination
  • Containerized the stack with Docker for portable deployment

  • Java
  • Spring Boot
  • React
  • Oracle DB
  • Redis
  • Docker
  • WebSockets
  • Azure AI

  Visit quicmov.com(opens in a new tab)
• 02

  Shipped2026

  SmartMeal

  Full Stack & ML Engineer

  A full-stack meal planning platform that turns what's already in your kitchen into recipes. It tracks inventory, shopping lists and budget, and recommends dishes that prioritise leftovers and near-expiry ingredients.

  • Built a recipe recommender using TF-IDF and cosine similarity over ingredients, dietary preferences, leftovers and near-expiry items
  • Integrated an AI chatbot and recipe generator on Azure AI for personalised, conversational suggestions
  • Implemented FastAPI backend services, Firebase authentication and notification management
  • Shipped inventory, shopping list, budget management and an admin dashboard

  • Python
  • FastAPI
  • React
  • MongoDB
  • Azure AI
  • Firebase
• 03

  Shipped2026

  Agro Lanka

  ML & Mobile Engineer

  A smart farming app for Sri Lankan farmers. Point a phone camera at a diseased crop and a CNN returns the diagnosis, confidence score, treatment and prevention guidance — alongside crop management, advisory alerts and a marketplace.

  • Developed CNN-based plant disease detection with TensorFlow and OpenCV, including the image preprocessing and ML pipelines
  • Designed FastAPI inference endpoints returning disease predictions, confidence scores, treatments and prevention guidance
  • Built the React Native frontend with crop management, advisory alerts, marketplace and expert support
  • Backed it with MongoDB, authentication and scalable REST APIs for real-world deployment

  • Python
  • FastAPI
  • TensorFlow
  • OpenCV
  • React Native
  • MongoDB
• 04

  Research2025

  Multi-Class Brain Tumor Classification

  Machine Learning Engineer

  A complete ML pipeline that classifies MRI scans into four tumor categories, from raw scan preprocessing through architecture comparison to a tuned final model.

  • Built the full pipeline classifying MRI scans into four tumor categories
  • Designed preprocessing including denoising and resizing for consistent model input
  • Applied data augmentation to improve robustness on a limited medical dataset
  • Compared multiple architectures and optimized an SVM classifier to high accuracy
  • Used EfficientNet transfer learning for feature extraction

  • Python
  • TensorFlow
  • OpenCV
  • CNN
  • scikit-learn

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