I build intelligent backend applications using Python, FastAPI, and modern AI technologies. My projects combine APIs, databases, LLMs, and automation to solve real-world business problems through scalable software solutions.
I'm a Computer Science graduate passionate about building intelligent software using Python and modern AI technologies. I enjoy designing backend systems, developing REST APIs, and integrating large language models into practical applications.
My work focuses on creating scalable AI-powered applications using FastAPI, PostgreSQL, React, and Docker. From Retrieval-Augmented Generation (RAG) systems to AI-assisted data quality platforms, I enjoy solving real-world problems through software.
I believe the best software combines clean architecture, automation, and great user experience. Every project I build aims to be practical, maintainable, and ready for real-world use.
Building scalable REST APIs using FastAPI and Python.
Developing intelligent applications powered by LLMs and LangChain.
Designing reliable data storage with PostgreSQL and SQLAlchemy.
Building full-stack applications with React, TypeScript and Docker.
An end-to-end AI-powered web application that automates dataset profiling, quality validation, intelligent recommendations, report generation, and data cleaning through a modern Python backend architecture.
AI Data Quality Guardian combines FastAPI, PostgreSQL, Docker, React, and LLMs to deliver automated dataset profiling, validation, AI-powered explanations, intelligent cleaning recommendations, audit logging, authentication, and PDF reporting within a production-style architecture.
This application is designed for local deployment using Docker Compose. The complete source code, setup guide, and documentation are available in the GitHub repository.
A collection of projects demonstrating Python backend development, AI applications, REST API design, and machine learning through scalable, real-world software solutions.
A Retrieval-Augmented Generation (RAG) application that enables semantic document search and intelligent question answering using local large language models. Built with FastAPI, LangChain, FAISS, Ollama, and Streamlit.
A machine learning application that detects deceptive interface patterns on e-commerce websites using NLP techniques, automated web scraping, and XGBoost. The project also maps findings to regulatory guidelines and visualizes insights through interactive dashboards.
An NLP-based machine learning application that identifies fraudulent business reviews using TF-IDF feature extraction and Random Forest classification. Deployed as an interactive Streamlit application.
Developed a fraud detection model using machine learning to identify suspicious financial transactions through behavioral and transaction pattern analysis.
Technologies and tools used for data analysis, business intelligence, machine learning, and data quality solutions.
Jyothi Engineering College, Kerala
Graduated 2025
Interested in Python backend development, AI applications, or intelligent automation? I'm always open to discussing opportunities, collaborating on innovative projects, or connecting with fellow developers.