Ahamed Shahmi
Full-Stack AI Engineer | RAG Pipelines • LLM Apps • Chatbot
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AI Agent for Automatic Book Summary Generation
An AI-powered agent that automatically generates summaries for PDF books using Large Language Models (LLMs).
The system continuously monitors a Google Drive folder, detects newly uploaded PDFs in real time, and generates concise summaries without manual intervention.
This project combines workflow automation using n8n with a FastAPI backend to demonstrate a production-style AI agent architecture.
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Multi-Modal AI System for Produce Freshness Detection
Designed and deployed a real-time multi-modal AI System integration CNN-based image analysis with gas sensor, NIR, and metadata features using early fusion. Achieved 93.42% accuracy with 113 ms interference latency on a portable edge device, outperforming image-only and sensor-only baselines across Accuracy, Precision, Recall, and F1-score.
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Intelligent resume analysis powered by Gemini 2.5, all-MiniLM-L6-v2, FastAPI, and React + Vite. Multi-dimensional ATS scoring, keyword matching, semantic similarity, skill gap detection, and actionable recommendations — all via a clean REST API.
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Built an agentic RAG chatbot enabling natural language conversations over 25 landmark AI/ML research papers from ArXiv, with source-attributed answers and page-level citations.
Designed a 5-category intent classification system using zero-temperature Gemini to route messages - eliminating unnecessary vector.
Implemented hybrid retrieval combining FAISS vector search (60%) and BM25 keyword search (40%) using LangChain EnsembleRetriever, improving recall for both semantic queries and exact terminology.
Architected a two-layer memory system - short-term session memory and long-term cross-session memory, injected into the system prompt for personalised responses.
Deployed FastAPI backend on AWS EC2 with Ngrok HTTPS tunnel and React frontend on Vercel.
Technologies: Python, FastAPI, LangChain, HuggingFace Transformers, FAISS, BM25, Google Gemini, SQLite, React, AWS EC2, Vercel.
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