Machine Learning Engineer Intern

Description

• •Designed and optimized a Retrieval-Augmented Generation (RAG) system for PDF analysis and question answering over documents exceeding 200+ pages.
• •Improved model precision by approximately 95% through optimized embeddings, retriever tuning, and prompt engineering.
• •Built AI-powered user interfaces using Python, TensorFlow, and Streamlit, enabling real-time querying with sub-second response times.
• •Prepared the system for deployment by optimizing inference pipelines and reducing response latency by ~30%.