Gnanaprasoona Mandan

I’m a Generative AI and Machine Learning engineer specializing in Python, LLMs, Retrieval-Augmented Generation (RAG), NLP, deep learning, and SQL. I focus on turning messy data into reliable signals through end-to-end pipelines, strong validation, and measurable business impact.

At Capgemini Technology Services Pvt. Ltd., I designed and executed SQL-driven automated data pipelines across 50,000+ records, improving analytics documentation efficiency by 20% and data accuracy by 35%. I also built 100+ validation test cases to ensure source-to-target consistency for enterprise AI/analytics simulation systems.

Before that, as a Data Scientist Intern at Coapps, I engineered an NLP-driven product recommendation and retrieval system for 6,000+ unstructured records, boosting relevance by 30%. I reduced search latency and processing time by 40% by building embedding and similarity search pipelines with Python and SQL, and improved content categorization automation by 25% through preprocessing and vectorization.

I’m especially passionate about leveraging Generative AI, LangChain, and advanced NLP techniques to solve complex business challenges. From an RAG & NLP recommendation engine that improved relevance by 30% to an ensemble-based rheumatoid arthritis detection model reaching 95% classification accuracy, I enjoy building solutions that are both technically strong and ready for real-world use.