We are looking for an experienced freelancer to develop a Proof of Concept (PoC) for an AI/ML solution incorporating **Retrieval-Augmented Generation (RAG)**. The goal is to build a prototype that combines machine learning with contextual data retrieval to generate accurate, relevant, and dynamic responses. **Objectives:** * Develop an AI/ML prototype enhanced with RAG architecture * Enable the system to retrieve relevant information from a knowledge base and generate intelligent outputs * Validate the effectiveness, accuracy, and scalability of the solution * Demonstrate real-world use cases such as Q&A systems, document search, or intelligent assistants **Scope of Work:** * Understand project requirements and define system architecture * Design and implement a RAG pipeline (retriever + generator) * Prepare and preprocess datasets / documents (PDFs, text, structured data, etc.) * Implement vector database (e.g., FAISS, Pinecone, Chroma, etc.) * Integrate embedding models and LLM (OpenAI, open-source models, etc.) * Develop and train/tune models where necessary * Evaluate system performance (accuracy, relevance, latency) * Build a simple demo interface or API to showcase functionality * Document the entire workflow and architecture **Deliverables:** * Working RAG-based AI/ML prototype * Source code with clear documentation * Configured vector database and retrieval pipeline * Performance evaluation report (retrieval + generation quality) * Final report with insights and next-step recommendations * (Optional) Demo UI or API endpoint **Required Skills:** * Strong experience in AI/ML and NLP * Hands-on experience with RAG pipelines * Proficiency in Python and ML frameworks * Experience with vector databases (FAISS, Pinecone, Weaviate, etc.) * Familiarity with LLMs (OpenAI API, Hugging Face models, etc.) * Knowledge of embeddings and semantic search **Preferred Qualifications:** * Experience building chatbots or knowledge-based AI systems * Familiarity with LangChain / LlamaIndex or similar frameworks * Experience deploying AI systems (cloud or local) * Understanding of prompt engineering and optimization'