Focused on Artificial Intelligence, Natural Language Processing, and Full Stack Engineering.
Building end-to-end software systems that combine machine learning with practical applications.
I'm a third-year Computer Science undergraduate at JNTUH University College of Engineering, Science and Technology, Hyderabad, pursuing a degree in Computer Science and Engineering.
My primary interests lie in Artificial Intelligence, Machine Learning, Natural Language Processing, and Full Stack Development. I enjoy designing software that combines modern AI techniques with scalable backend systems, taking projects from model development and experimentation through deployment.
Most of my work focuses on building practical AI applications, including LLM-powered systems, semantic search pipelines, browser extensions, and database-driven web platforms that solve real-world problems.
AI-powered Chrome extension that analyses political bias in Indian news articles using a hybrid machine learning and LLM pipeline. The system combines IndicBERT, a custom Liquid Neural Network, semantic vector search, and Groq LLMs to provide bias scores, alternative perspectives, and cross-article comparisons.
- Designed and trained a custom bias-classification pipeline using IndicBERT and Liquid Neural Networks
- Built the FastAPI backend and API architecture
- Developed the complete machine learning training pipeline
- Implemented ChromaDB semantic retrieval with topic-anchor filtering
- Integrated Groq LLM for explainable bias analysis
- Containerised and deployed the application using Docker
- Contributed to the Chrome extension functionality while collaborating on the overall project
Tech Stack
Python
FastAPI
PyTorch
Hugging Face
IndicBERT
Groq
ChromaDB
Docker
Chrome Extension
AI-powered onboarding platform that analyses resumes against job descriptions and automatically generates personalised learning roadmaps using local LLMs.
- Developed the complete FastAPI backend
- Designed the AI pipeline for resume parsing and skill-gap analysis
- Integrated Llama 3.2 locally using Ollama
- Built the adaptive learning-path generation algorithm
- Implemented PDF parsing and O*NET skill standardisation
- Collaborated with teammates during overall system integration while the frontend was developed separately
Tech Stack
Python
FastAPI
LangChain
Ollama
Next.js
Chrome extension that compares the same news event across multiple political perspectives using semantic search and LLM reasoning.
- Designed the backend architecture
- Implemented the Groq-powered political bias detection pipeline
- Built the ChromaDB semantic retrieval workflow
- Integrated NewsAPI and Guardian API fallback mechanisms
- Developed the article comparison workflow
- Collaborated on the Chrome extension implementation
Tech Stack
Python
FastAPI
Flask
Groq
ChromaDB
Chrome Extension
An object-oriented implementation of Linear Regression built entirely from mathematical foundations without relying on machine learning frameworks.
- Implemented variance and covariance calculations from scratch
- Designed the LinearRegression class using object-oriented programming
- Implemented prediction and Mean Squared Error evaluation
- Visualised predictions using Matplotlib
Tech Stack
Python
Matplotlib
Enterprise-style railway reservation platform supporting authentication, bookings, payments, seat allocation, and waitlist management.
- Designed and implemented the Oracle SQL database schema
- Created relational tables, constraints, and database relationships
- Worked on database normalization
- Assisted with backend database integration
Tech Stack
Oracle SQL
React
Node.js
Express
- Retrieval-Augmented Generation (RAG)
- Agentic AI Systems
- Transformer Architectures
- MLOps
- Distributed Backend Systems
- Scalable AI Deployment
- 🚀 Building Campus Loop, an AI-powered academic collaboration platform
- 🤖 Developing practical AI applications powered by LLMs
- 📚 Strengthening machine learning engineering and backend development skills
- 💼 Preparing for Software Engineering and AI internship opportunities
