I’m Ajeet Kumar Upadhyay, a Generative AI Engineer with 2+ years of software development experience and dual IBM certifications in Gen AI Engineering and RAG/Agentic AI. I specialize in building production-ready RAG systems, autonomous AI agents, and multimodal applications using LangChain, LangGraph, and multi-agent frameworks.
I’m proficient in LLMs, transformers, vector databases, and prompt engineering, with strong Python and full-stack development skills. I enjoy turning complex AI concepts into reliable, scalable solutions that empower teams to ship faster.
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Project URL: https://www.twine.net/signin
◦ Developed semantic search RAG application for querying YouTube video transcripts with context-aware answers
◦ Built automated pipeline for transcript extraction, text chunking, embedding generation, and vector storage
◦ Implemented retriever with similarity search using sentence-transformers and ChromaDB for efficient retrieval
◦ Deployed live Streamlit application processing user queries with Groq LLM inference
◦ Built production-ready multi-document RAG system with fact verification and hybrid retrieval (keyword + semantic)
◦ Implemented multi-agent architecture with relevance checking to eliminate hallucinations and out-of-scope responses
◦ Integrated Docling parser supporting PDF, DOCX, TXT, and Markdown with ChromaDB vector storage
◦ A Gradio web interface with real-time query processing using Groq LLM and HuggingFace embeddings
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