TailorCV.ai
Developed an AI web application that optimizes resumes to job descriptions using LLMs and NLP pipelines, improving resume relevance by up to 80%. Designed a Python and FastAPI backend with HTML, CSS, and JavaScript for the frontend, dockerized the application, and deployed it on AWS ECS. Achieved over 50 users within the first week of launch, demonstrating strong early adoption and real-world impact.
Tech Stack: Python, FastAPI, LLM, AI agents, Amazon Web Services
YouTube Sentiment Analysis
Created an end-to-end YouTube sentiment analysis pipeline processing over 10,000 user comments, enhancing sentiment classification performance through NLP preprocessing techniques. Tracked multiple model experiments using MLflow and DVC, enabling reproducible training and systematic comparison of models built with scikit-learn and NLP libraries. Deployed the pipeline on AWS using Docker and exposed predictions via Flask REST APIs, facilitating scalable and reproducible inference.
Tech Stack: TensorFlow, NLP, AWS EC2, Scikit-learn
Smart Product Pricing
Developed an NLP and CV pipeline to analyze 150,000 image and text data using transformer-based text encoders and CNN-based image embeddings, integrating them through a fusion neural network for price prediction. Implemented data preprocessing techniques, including text cleaning, tokenization, and streaming image feature extraction with ResNet and CLIP representations to manage large datasets. Built and fine-tuned models using TensorFlow and scikit-learn, achieving a rank of 142 out of 50,000 participants.
Tech Stack: Keras, Hugging Face Transformers, ResNet50, OpenCV
RAG System
Made a production-ready RAG pipeline integrating semantic vector retrieval with LLM generation to produce context-grounded responses. Engineered multiple chunking strategies and a scalable ingestion , retrieval , generation flow for efficient semantic search and generation. Implemented history-aware and multimodal augmentations, and evaluated retrieval outputs to measure relevance and quality.