Tailorcv.ai
Developed an AI web application that customizes resumes to align with job descriptions, enhancing resume relevance by up to 80%. Engineered a Python and FastAPI backend, complemented by HTML, CSS, and JavaScript for the frontend, deployed on AWS. Achieved over 50 users within the first week of launch, demonstrating strong early adoption and real-world impact.
Tech Stack: AI web app utilizing LLMs and NLP pipelines
Youtube Sentiment Analysis
Created a comprehensive YouTube sentiment analysis pipeline processing over 10,000 user comments, enhancing sentiment classification performance through advanced NLP preprocessing techniques. Tracked multiple model experiments using MLflow and DVC, facilitating reproducible training and systematic comparison of models developed with scikit-learn and NLP libraries. Deployed the pipeline on AWS utilizing Docker, exposing predictions via Flask REST APIs for scalable and reproducible inference.
Tech Stack: End-to-end sentiment analysis pipeline
RAG System
Developed 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, and generation flow for efficient semantic search and generation. Implemented history-aware and multimodal augmentations, evaluating retrieval outputs to measure relevance and quality.
Tech Stack: Production-ready RAG pipeline
Smart Product Pricing Model, Amazon ML Challenge 2025
Created 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. Applied feature engineering, outlier handling, and SMAPE-based evaluation to optimize prediction accuracy, achieving a rank of 142 out of 50,000 participants.
Tech Stack: NLP and CV pipeline for price prediction