Ansh Mangukiya
AI Engineer | RAG, AI Agents & LLM Applications
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AthletiX Hub — Conversational AI E-Commerce Platform
Built a full-stack fitness supplement e-commerce platform with an integrated conversational AI shopping assistant.
AthletiX Hub combines a Flask backend, MySQL database, responsive web interface, and Dialogflow-powered chatbot to create a conversational shopping experience. Users can browse products and interact with the AI assistant using natural language to add or remove supplements, complete orders, calculate totals, generate order IDs, and track existing orders.
I developed the backend business logic, database integration, conversational workflow, session-based order management, webhook handling, and interactive frontend components.
The project demonstrates how conversational AI can be integrated directly into real e-commerce workflows rather than being limited to simple question-answering.:
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NLP Studio — Interactive Natural Language Processing Platform
Built an interactive NLP platform that brings multiple natural language processing capabilities into a single application. The system performs sentiment analysis, Named Entity Recognition (NER), Part-of-Speech (POS) tagging, and text processing on real-world text data.
I developed the complete application, including the NLP processing pipeline, model integration, backend APIs, and user interface. The project uses Python, spaCy, Scikit-learn, Pandas, and Flask to transform raw text into structured and actionable insights.
The project demonstrates my ability to take NLP models and techniques beyond experimentation and turn them into an interactive, usable AI application.
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AI-Powered Resume & Career Matching System:
Built an AI-powered career matching platform that analyzes candidate profiles and job requirements to identify relevant career opportunities.
The system applies Natural Language Processing and Machine Learning techniques to extract meaningful information from resumes and job descriptions, compare candidate skills with role requirements, identify skill gaps, and generate personalized career recommendations.
The project combines data preprocessing, feature engineering, NLP-based text analysis, similarity matching, and an interactive application layer to turn unstructured career data into actionable insights.
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Built and deployed my AI Engineering portfolio featuring a production-ready RAG assistant that lets visitors interact with an AI trained on my professional experience, projects, skills, and technical background.
The assistant uses FastAPI, LangGraph, Qdrant, Groq, and Python to retrieve relevant context and generate accurate, conversational responses. The portfolio itself is built with Next.js and Tailwind CSS, with the AI backend deployed separately and integrated through a live API.
This project demonstrates my ability to build and deploy complete GenAI applications—from vector search and RAG pipelines to backend APIs and production frontend integration.
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