Integrate AI (RAG / Vectorless RAG) + Socket.IO into Existing Node.js, React & Python Application

We are looking for an experienced AI/Full-Stack engineer to integrate an AI-powered Retrieval-Augmented Generation (RAG) system along with real-time communication using Socket.IO into our existing application built with Node.js, React, and Python services. The goal is to enhance the platform with intelligent, context-aware AI responses while enabling real-time streaming, notifications, and live interactions between backend services and the frontend. Key Responsibilities Design and implement a scalable AI RAG (Retrieval-Augmented Generation) pipeline Integrate application data sources (databases, documents, APIs) into a vector database Implement embedding generation, chunking strategies, and retrieval logic Integrate LLMs (OpenAI or similar AI models) for intelligent responses Implement Socket.IO for real-time communication Live AI response streaming Real-time notifications Dynamic frontend updates Build and maintain backend services using Node.js and/or Python (FastAPI) Ensure low-latency, scalable, and production-ready AI architecture Integrate APIs with existing React frontend Optimize performance, cost, and accuracy of AI + retrieval pipeline Required Skills Strong experience with AI RAG architectures Proficiency in Node.js (NestJS/Express) and/or Python (FastAPI) Experience with Socket.IO (real-time systems) Knowledge of vector databases (Pinecone, Weaviate, FAISS, pgvector) Hands-on experience with OpenAI API or other AI/LLM models Understanding of embeddings, chunking, and retrieval strategies Experience with REST APIs and microservices architecture Docker and deployment experience Nice to Have Experience with LangChain / LlamaIndex Hybrid search (keyword + vector search) Experience optimizing LLM latency and cost SaaS or AI product integration experience Deliverables Fully working AI RAG pipeline integrated into existing system Socket.IO-based real-time communication layer Backend APIs for AI query handling and responses Vector database setup and ingestion pipeline Integration with React frontend (real-time UI updates) Documentation + deployment guide Project Type Fixed price or hourly (to be discussed) Phase 1: MVP integration (AI RAG + basic Socket.IO flow) Phase 2: Scaling, optimization, and enhancements To Apply, Please Include Relevant experience with AI RAG systems and real-time applications Examples of similar AI / Socket.IO / backend projects Your preferred tech stack for vector DB + AI integration Estimated timeline for MVP delivery