I'm a passionate MERN Stack & ML Developer with a strong grip on building scalable web apps and intelligent systems.
Alongside crafting APIs and scalable backends, I'm exploring Machine Learning to bridge the gap between software engineering and data-driven solutions.

• 🌐 Build scalable full-stack apps with MongoDB, Express, React, Node.js (MERN)
• ⚙️ Design and optimize RESTful APIs & GraphQL services
• 🔐 Implement secure authentication (OAuth2, JWT, RBAC)
• 🧠 Develop ML models for classification, regression & NLP
• 🤖 Deploy ML models as APIs with FastAPI / Flask
• 🚀 Deploy and scale apps using Vercel, Render, Railway, Back4App
• 📦 Architect microservices and cloud-ready backends

Full-stack MERN platform for sharing academic resources

• 🔐 JWT-based authentication with password reset via email
• 📂 File upload (PDF, DOCX, PPTX, images) with AWS S3 integration
• ⭐ Social features: upvotes, comments, and user dashboards
• 🔍 Search & filter by department, semester, or file type
• ⚙️ Monorepo architecture with separate frontend & backend deployments
• 🧩 Built with React, Node.js, TypeScript, MongoDB, Tailwind, AWS S3

A modern, scalable e-commerce platform for buying and selling secondhand goods.

• 🔐 Secure user authentication with JWT and password hashing
• 🛒 Order management system with tracking of customer orders and status
• 📦 Cart functionality with the ability to add, update, and remove items from the cart
• 💳 Integration with payment gateways for processing orders
• 🛒 Item listing and categorization for easy browsing
• 💬 Real-time messaging feature between buyers and sellers
• 📦 Product image upload and storage integration with Cloudinary
• 🛡️ Role-based access control for admin and user privileges
• 📊 Optimized database queries for fast item retrieval
• 📍 Location-based item search for nearby deals
• 🚀 Efficient caching for faster response times
• 📝 Complete CRUD operations for item management

ML-powered system for classifying network events or detecting intrusions (e.g., malicious vs benign traffic)

• Dataset: [your dataset name], features include [list key features, e.g., packet size, flags, protocol]
• Models: [e.g., Random Forest, Isolation Forest, Neural Network, etc.]
• Pipeline: data preprocessing → feature engineering → model training → evaluation (metrics like accuracy, F1-score)
• Deployment: served via FastAPI / Flask, containerized with Docker, optionally using MLflow for experiment tracking
• Monitoring / UX: simple API/UI (could be Streamlit, plain endpoints) for real-time inference & alerts

ML-powered web app that predicts heart disease risk in real time

• 🧠 Logistic Regression model using Scikit-learn, trained on patient health metrics (age, cholesterol, blood pressure, ECG data, and more)
• 🌐 FastAPI backend with intuitive, mobile-responsive frontend (HTML + JS + Tailwind CSS)
• 🎨 Rich visualizations with Matplotlib, Seaborn, and Chart.js for confidence levels and risk insights
• 🚀 Deployed on Render with Docker, Procfile, and render.yaml for smooth deployments

Streamlit-based RAG chatbot that lets users upload PDFs and chat with their content

• RAG pipeline via LangChain + HF embeddings + Chroma vector store
• Conversational PDF querying with session-aware chat history
• Built with Streamlit, HuggingFace, LangChain, ChromaDB

• 🤖 Building ML APIs & deploying models
• 🧠 Deepening knowledge in NLP and Computer Vision
• 📚 Exploring MLOps and scalable ML deployment
• 🌐 Exploring microservices architectures
• 🔍 Contributing to open-source projects
• 🏆 Participating in hackathons and tech competitions
• 💡 Building scalable TypeScript applications

• 🐦 Twitter: Share tech insights and project updates @viraj_gavade
• 📸 Instagram: Behind the scenes of my coding journey @_viraj.js
• 💼 LinkedIn: Professional network and career updates Viraj Gavade

I'm always interested in collaborating on innovative projects and discussing tech! Feel free to reach out through any of the social links above or drop me an email at vrjgavade17@gmail.com.