I am a full-stack developer who enjoys building practical, reliable products from idea to deployment. My interests span responsive frontend interfaces, scalable backend services, AI-powered features, cloud infrastructure, DevOps practices, and system design.

I like understanding how every part of a product works together—from the user experience and API design to databases, deployments, networking, and security. My goal is to build software that is clean, useful, maintainable, and ready to grow.

• 🏗️ Building scalable full-stack applications with maintainable architecture
• 🤖 Applying AI and machine learning to practical real-world problems
• ☁️ Learning AWS, cloud deployment, DevOps workflows, and distributed systems
• 🔐 Strengthening networking knowledge: DNS, routing, switching, and security

🏢 Software Development Intern — NST Pvt. Ltd.

At NST Pvt. Ltd., I contributed to EaseExpense, a full-stack daily expense-tracking application designed to make personal-finance management easier and more proactive.

My work included building budget-focused features such as automated spending-limit alerts and monthly receipt-style summary emails. I also collaborated on debugging, testing, and refining the application in a real product-development environment. The internship strengthened both my development skills and my understanding of networking fundamentals including DNS, routing, switching, and network security.

A full-stack expense tracker built during my internship at NST Pvt. Ltd. It helps users record daily spending, understand their monthly habits, and stay within a planned budget. • Sends immediate email alerts when expenses exceed budget limits • Delivers clear monthly receipt-style summaries • Focused on fast, simple, daily expense logging Stack: Node.js · React · Supabase	An IoT and machine-learning system that monitors sensor data to predict machine health and estimate Remaining Useful Life for predictive maintenance. • Collects real-time sensor data through ESP32 • Includes data preparation, ML training, and visualization • Developed as a sixth-semester IoT case study Stack: ESP32 · Python · React · Flask · Scikit-learn Repository →
A personalized AI assistant that routes user questions through multiple integrated APIs to provide helpful, natural, and responsive conversations. • Modern responsive interface with a scalable backend structure • Designed for low-latency, natural-feeling interactions Stack: React · Node.js · TypeScript · Tailwind CSS Repository → · Live Demo →	A phishing-URL detection platform that combines threat intelligence, domain information, heuristic checks, and machine learning for layered protection. • Uses VirusTotal, pattern analysis, domain intelligence, and Random Forest ML • Designed to avoid reliance on one single security signal Stack: Next.js · Python · FastAPI · Scikit-learn Repository → · Live Demo →
An AI-powered recruitment platform where an intelligent interviewer conducts realistic technical interviews and adapts questions based on candidate responses. • Dynamic technical questions and contextual follow-ups • Architecture prepared for interview analytics, feedback, and history tracking Stack: React · Node.js · FastAPI · Tailwind CSS · AI APIs  |  🚧 In development

Responsive interfaces, component-based UI development, and accessible web experiences.	API design, server-side logic, data flow, and reliable application services.
Intelligent features, data processing, model development, and machine-learning experimentation.	Database management, cloud services, containers, and deployment-ready infrastructure.
Core programming languages used for application, web, and problem-solving development.	Version control, development environments, deployment platforms, and production tooling.