As an AI Software Engineer with a strong foundation in backend development, I specialize in creating scalable, innovative solutions for real-world challenges. My experience spans cybersecurity, AI, and healthcare technology, where I’ve developed systems that enhance security and user engagement. Starting as a Python Developer, I focused on integrating blockchain for secure healthcare transactions and modernizing legacy systems for improved reliability. As a Backend Developer, I designed microservices for AI platforms, improving data accuracy and efficiency through seamless API integration and cloud-native infrastructure. In my current role, I protect businesses from online identity fraud by optimizing AI applications using Python and Node.js, managing AWS infrastructure, and refining solutions with Docker and Kubernetes. I actively contribute to Agile processes, mentor junior engineers, and maintain high standards through rigorous code reviews and documentation. Passionate about continuous improvement, I aim to connect with professionals to drive advancements in technology and security. Let’s collaborate to innovate and secure our digital future.
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Objectives
Build a Multi-Agent System: Implement a framework that allows multiple AI agents to collaborate and provide comprehensive responses to user inquiries.
Integrate RAG: Utilize Retrieval-Augmented Generation to enhance the AI’s ability to pull relevant information from a knowledge base, improving the accuracy and relevance of responses.
User-Friendly Interface: Develop a web-based interface using Django to ensure seamless interaction between users and the AI agents.Technologies Used
Python: The primary programming language for backend development and AI model integration.
Django: A high-level web framework for building the application’s web interface and managing server-side logic.
OpenAI Multi-Agent: To create diverse agents, each specializing in different domains, enhancing the overall conversational experience.
RAG (Retrieval-Augmented Generation): To enable the AI to fetch and generate responses based on real-time data from a curated knowledge base.Features
Natural Language Understanding: The system will interpret user inputs in natural language, allowing for fluid conversations.
Contextual Awareness: Agents will maintain context throughout interactions to provide coherent and relevant responses.
Knowledge Retrieval: The RAG component will allow agents to access external information sources, ensuring up-to-date and accurate answers.
Scalability: Designed to support multiple users and expand the number of agents as needed.
Overview
The Conversational AI Agent project aims to develop an intelligent and interactive system that can engage users in natural language conversations. Leveraging advanced technologies such as Python, Django, OpenAI’s multi-agent architecture, and Retrieval-Augmented Generation (RAG), this project will create a robust AI-driven platform capable of understanding and responding to user queries effectively.
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