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

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