I'm a Machine Learning Engineer and Full-Stack Developer specializing in building production-ready AI systems and scalable web applications. I transform complex data into intelligent solutions while crafting robust, user-centric platforms that bridge the gap between cutting-edge ML models and real-world business needs.
- 🧠 Design & deploy end-to-end ML pipelines for classification, regression, NLP & computer vision
- 🤖 Build production ML APIs with FastAPI, Flask, and model serving infrastructure
- 🌐 Architect scalable full-stack applications with MERN stack (MongoDB, Express, React, Node.js)
- ⚙️ Design RESTful APIs & GraphQL services optimized for performance
- 🔐 Implement enterprise-grade security (OAuth2, JWT, RBAC, AES-256 encryption)
- 📊 Create data pipelines and MLOps workflows for model deployment & monitoring
- ☁️ Deploy & scale apps using AWS, Vercel, Render, Railway, Docker
- 🏗️ Build microservices architectures and cloud-native solutions
Production-ready educational resource sharing platform
A comprehensive MERN stack application empowering students to collaboratively share and discover academic materials.
Key Features:
- 🔐 Secure JWT authentication with email-based password recovery
- 📂 Multi-format file uploads (PDF, DOCX, PPTX, images) with AWS S3 integration
- ⭐ Social engagement: upvotes, comments, and personalized user dashboards
- 🔍 Advanced search & filtering by department, semester, and file type
- 📊 Real-time analytics for resource popularity and user engagement
- ⚡ Optimized MongoDB queries with indexing for fast retrieval
- 🏗️ Monorepo architecture with separate frontend/backend deployments
Tech Stack: React • Node.js • Express • TypeScript • MongoDB • Tailwind CSS • AWS S3 • JWT
Modern e-commerce marketplace for secondhand goods
Scalable platform connecting buyers and sellers of pre-loved items with real-time communication.
Key Features:
- 🔐 Secure authentication with bcrypt password hashing and JWT tokens
- 💳 Integrated payment gateway for seamless order processing
- 🛒 Dynamic cart system with real-time updates and inventory management
- 💬 Real-time messaging between buyers and sellers (WebSocket/Socket.io)
- 📦 Cloudinary integration for optimized image storage and delivery
- 🛡️ Role-based access control (RBAC) for admin and user privileges
- 📍 Location-based item discovery for finding nearby deals
- 🚀 Redis caching layer for enhanced performance
- 📊 Complete CRUD operations with transaction support
Tech Stack: React • Node.js • Express • MongoDB • Socket.io • Cloudinary • Redis • Stripe
📚 CodesHub
Collaborative platform for sharing code and academic resources
Full-stack solution enabling students to share, discover, and collaborate on code implementations and study materials.
Tech Stack: React • Node.js • Express • MongoDB • Redux • REST API
Intelligent attendance tracking and analytics system
Next.js-powered dashboard providing insights into student attendance patterns and academic performance.
Key Features:
- 📈 Interactive visualizations with Chart.js for trend analysis
- 🤖 Automated report generation with predictive analytics
- 🎯 Server-side rendering for optimal performance
- 📱 Responsive design for mobile and desktop
Tech Stack: Next.js • MongoDB • TypeScript • Chart.js • Tailwind CSS
Privacy-first password management solution
Lightweight, security-focused password manager with zero-knowledge architecture.
Key Features:
- 🔒 Client-side AES-256 encryption ensuring complete privacy
- ☁️ Secure cloud synchronization without server-side access to passwords
- 🚀 Fast, minimal UI built with Next.js and TypeScript
- 🛡️ Zero-knowledge design: your data, your encryption keys
Tech Stack: Next.js • TypeScript • MongoDB • Crypto-JS • Tailwind CSS
Deep learning system for automated music emotion recognition
Advanced audio classification model trained to detect emotions in music with high accuracy.
Technical Highlights:
- 🎼 Audio signal processing with MFCC, spectrograms, and chromagrams
- 🧠 Custom CNN architecture optimized for audio feature extraction
- 📊 Multi-class classification (happy, sad, energetic, calm, etc.)
- ⚡ Model optimization with PyTorch for production deployment
- 📈 Comprehensive evaluation with precision, recall, F1-score metrics
Tech Stack: PyTorch • Librosa • NumPy • Scikit-learn • Matplotlib • Audio Processing
Production ML model serving platform
RESTful API serving PyTorch models for wine quality assessment (regression & classification).
Technical Highlights:
- 🚀 FastAPI backend with async request handling
- 🔄 Model versioning and A/B testing support
- ✅ Input validation with Pydantic schemas
- 📖 Auto-generated Swagger UI documentation
- 🐳 Dockerized deployment for consistent environments
- 📊 Real-time inference with sub-100ms latency
Tech Stack: FastAPI • PyTorch • Pydantic • Docker • Uvicorn
Computer vision pipeline for aerial imagery analysis
End-to-end system for detecting and classifying objects in drone/satellite imagery.
Technical Highlights:
- 🎯 State-of-the-art object detection with YOLO/Faster R-CNN
- 🔄 Custom data augmentation pipeline for robust training
- 📊 Comprehensive metrics: mAP, precision, recall at multiple IoU thresholds
- ⚡ Optimized for real-time inference on edge devices
- 🗺️ Geospatial data integration for location-aware predictions
Tech Stack: PyTorch • OpenCV • YOLO • Computer Vision • Data Augmentation
Intelligent cybersecurity threat detection pipeline
ML-powered system for identifying and classifying network intrusions and malicious traffic.
Technical Highlights:
- 🧠 Ensemble methods: Random Forest, XGBoost, Neural Networks
- 🔍 Advanced feature engineering on network packet data
- 📊 SHAP explainability for model transparency
- ⚡ Real-time threat scoring with confidence intervals
- 🎯 Class imbalance handling with SMOTE
- 📈 MLOps workflow with experiment tracking
Tech Stack: Scikit-learn • XGBoost • SHAP • Feature Engineering • MLOps
ML solution for credit risk assessment
Comprehensive fraud detection and credit risk prediction for financial services.
Technical Highlights:
- 💰 Business-focused evaluation metrics (cost-benefit analysis)
- 🎯 Class imbalance handling with SMOTE and ensemble methods
- 🔄 Hyperparameter optimization with GridSearchCV/Optuna
- 📊 Feature importance analysis for regulatory compliance
- 🚀 Production-ready pipeline with data validation
Tech Stack: Python • Scikit-learn • Pandas • Imbalanced-learn • Model Tuning
Real-time cardiovascular risk assessment web app
ML-powered platform predicting heart disease risk from patient health metrics.
Technical Highlights:
- 🧠 Logistic Regression model with 85%+ accuracy
- 📊 13+ clinical features: age, cholesterol, BP, ECG, exercise data
- 🎨 Interactive visualizations with Matplotlib, Seaborn, Chart.js
- 🌐 Mobile-responsive UI with FastAPI backend
- 🐳 Docker containerization with Render deployment
- 📈 Confidence score visualization for risk assessment
Tech Stack: FastAPI • Scikit-learn • Matplotlib • Chart.js • Docker • Render
RAG-powered document Q&A system
Intelligent chatbot enabling natural language queries over PDF documents with memory.
Technical Highlights:
- 🧠 RAG (Retrieval-Augmented Generation) pipeline with LangChain
- 📚 HuggingFace embeddings + ChromaDB vector store for semantic search
- 💬 Session-aware conversation history for context retention
- 📄 Multi-PDF support with efficient chunk processing
- 🎨 Clean Streamlit interface for easy interaction
- 🔍 Source citation for answer traceability
Tech Stack: Streamlit • LangChain • HuggingFace • ChromaDB • Python
Neural network architecture exploration
Comprehensive study of ANN architectures for supervised learning tasks.
Technical Highlights:
- 🔬 Experimentation with various network architectures
- 📊 Hyperparameter tuning and optimization strategies
- 📓 Detailed Jupyter notebooks with visualizations
- 📈 Performance comparison across configurations
Tech Stack: TensorFlow/Keras • Jupyter • Neural Networks • Hyperparameter Tuning
- 🧠 Advanced MLOps: Building scalable model deployment pipelines with monitoring and retraining
- 🔍 Computer Vision: Exploring YOLO, Vision Transformers, and segmentation models
- 💬 NLP & LLMs: Fine-tuning language models and building RAG applications
- 🏗️ Microservices: Designing distributed systems with event-driven architectures
- ☁️ Cloud-Native Development: Kubernetes, serverless, and infrastructure as code
- 🤝 Open Source: Contributing to ML and web development projects
- 🏆 Competitions: Participating in Kaggle competitions and hackathons
- 📚 Research: Staying current with latest ML research papers and implementations
- ✅ Production-Ready Solutions: Writing clean, maintainable, scalable code
- ✅ Full Product Lifecycle: From ideation to deployment and monitoring
- ✅ Business Impact Focus: Building features that drive measurable outcomes
- ✅ Cross-Functional Collaboration: Strong communication with technical and non-technical stakeholders
- ✅ Continuous Learning: Staying ahead of ML and engineering trends
- ✅ Problem-Solving Mindset: Breaking down complex challenges into actionable solutions
📧 Email: viraj17.dev@gmail.com
💼 LinkedIn: Viraj Gavade
🐦 Twitter: @viraj_gavade
📸 Instagram: @_viraj.js
🌐 Portfolio: portfolio-viraj-gavades-projects.vercel.app
I'm actively seeking roles where I can leverage my ML engineering and full-stack development expertise to build innovative products. Whether you're working on cutting-edge AI applications, scaling data pipelines, or building the next generation of intelligent platforms—let's talk!
Available for: Full-time positions • Contract work • Technical consulting • Open-source collaboration
⭐ Star my repositories if you find them helpful! | 🤝 Let's build something amazing together!
Happy Coding! 👨💻