I specialize in building advanced, end-to-end AI solutions using modern LLMs, computer vision, and agent-based architectures. My expertise includes NanoGPT finetuning, LoRA/QLoRA optimization, LangChain and LangGraph pipelines, chatbot agent orchestration, OpenCV computer vision systems, reinforcement learning models, Whisper finetuning, and intelligent automation workflows.
With a strong mathematical foundation and a research-driven mindset, I focus on designing models and systems that are not only accurate, but also efficient, scalable, and aligned with real-world use cases. Whether you need custom LLM training, RAG pipelines, model optimization, speech-to-text tuning, advanced CV modules, or fully orchestrated AI agents, I deliver clean, well-structured, and production-ready implementations.
From dataset preparation and research analysis to model training, evaluation, integration, and deployment, I handle the complete development lifecycle. My goal is to provide high-quality engineering with clear communication, fast delivery, and a strong focus on solving your problem the right way.
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This project implements an AI-powered restaurant chatbot that leverages Retrieval-Augmented Generation (RAG) to provide personalized food recommendations and assist users with their dining experience. The chatbot intelligently suggests dishes based on user preferences, dietary restrictions, and desired spice levels, while also providing drink and dessert recommendations.
Key Features:
Personalized Recommendations: Suggests vegetarian, non-vegetarian, spicy, kid-friendly, and allergen-free dishes.
Retrieval-Augmented Generation (RAG): Uses a knowledge base of menu items and ingredients to provide accurate, context-aware responses.
Interactive Chat Interface: Full-page web interface for modern chat applications, with user queries and AI responses
Allergen & Dietary Awareness: Ensures safe recommendations by filtering items based on dietary restrictions or allergies.
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