AI/ML Engineer Needed to Build a RAG-Based AI Document Assistant

**Job Description** We are looking for a skilled AI/ML Engineer to develop an AI-powered document assistant using Retrieval-Augmented Generation (RAG). The system should be capable of reading, understanding, and retrieving information from our company documents and providing accurate, detailed answers to user queries. **Key Responsibilities** - Design and develop a RAG-based AI system. - Build document ingestion and processing pipelines. - Support multiple document formats, including PDF, DOCX, and Excel. - Implement text extraction, chunking, and embedding generation. - Integrate a vector database for efficient information retrieval. - Integrate an LLM to generate accurate, context-aware responses. - Allow users to ask questions about company documents using natural language. - Generate document summaries and extract important information. - Provide relevant document references and citations with answers. - Optimize retrieval accuracy and minimize AI hallucinations. - Develop APIs for future application integration. **Required Skills** - Python - Machine Learning and Deep Learning - Large Language Models (LLMs) - Retrieval-Augmented Generation (RAG) - LangChain or LlamaIndex - Vector Databases (FAISS, ChromaDB, Pinecone, or similar) - Embeddings and Semantic Search - OpenAI API or Open-source LLMs - FastAPI **Expected Deliverables** 1. Fully functional RAG-based AI document assistant. 2. Document upload and processing functionality. 3. Natural-language question-answering system. 4. Document summarization and information extraction. 5. Source citations for generated answers. 6. Well-documented and maintainable source code. 7. Deployment instructions and technical documentation. **To Apply** Please share: - Your previous experience building RAG or AI document assistants. - Links to relevant projects or GitHub repositories. - Your proposed technology stack. - Estimated development timeline. - Your expected project cost. We are looking for someone with practical implementation experience who can deliver a reliable and scalable solution.