Amgoth Hrithik Pawar
About
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Aspiring Data Scientist | Building LLM-Powered RAG & GenAI Systems | Python • LangChain •LangGraph
Andhra Pradesh, India
Résumé
Jobs
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Data ScientistClinovoJul 2025 - Sep 2025 (3 months)• Developed an intelligent conversational assistant to automate resume processing and candidate profile updates. • Built a multi-turn chatbot using LangGraph for seamless conversation and context understanding. • Created a robust document extraction pipeline with PyMuPDF and PyTesseract OCR for accurate text extraction from various file formats. • Integrated LangChain with OpenAI LLMs and implemented Pydantic models for structured data extraction and validation.
Education
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- Bachelor of Technology, Electrical engineeringIndian Institute of Technology HyderabadJan 2017 - Dec 2021 (5 years)
Projects (professional or personal)
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- PProduct Retrieval System• Built an intelligent product search system that retrieves relevant items based on user-provided text or image queries. • Achieved accurate and efficient product recommendations in a dataset of 44k products by combining visual and textual features to improve search relevance. • Used CLIP Transformer with PyTorch for embedding computation, FAISS for similarity search. Integrated FastAPI backend to handle image uploads, text inputs, and serve top searches.
- AAgentic RAG System• Developed an AI assistant that extracts and summarizes document content in response to user queries. • Engineered a context-aware agent using LangChain and LangGraph to dynamically switch between document retrieval and answer generation. • Leveraged OpenAI Embedding with ChromaDB for semantic retrieval and integrated GPT-4o for accurate real-time summarization and response generation.
- EEmotion-Aware Food Order Tracking ChatbotDeveloped a food delivery assistant that helps users place, update, and track orders while responding empathetically based on their emotions. • Built a smart multi-intent chatbot capable of handling 9 core user intents with natural and context-aware interactions. • Utilized Google Dialogflow for natural language understanding (NLU) and FastAPI for smooth backend integration. • Implemented MySQL for order and user data management and integrated a RoBERTa-based emotion detection model to personalize responses based on user sentiment.