RAG (Retrieval-Augmented Generation) Instructor / Expert

We are looking for an experienced RAG Instructor or Expert to create and deliver high-quality training content focused on Retrieval-Augmented Generation systems and modern knowledge retrieval applications. Responsibilities: Develop a comprehensive curriculum covering RAG concepts, architectures, and implementation techniques. Create engaging video lectures, coding demonstrations, and hands-on projects. Teach topics such as document processing, embeddings, vector databases, semantic search, retrieval pipelines, and response generation. Build real-world applications involving knowledge bases, search systems, and intelligent assistants. Design practical assignments, projects, and assessments. Keep course content updated with the latest frameworks, tools, and industry practices. Requirements: Strong experience in building and implementing RAG applications. Proficiency in Python and modern development frameworks. Experience with vector databases, embeddings, and semantic search techniques. Knowledge of document ingestion, indexing, and retrieval workflows. Experience with frameworks such as LangChain, LlamaIndex, or similar tools. Understanding of API integration, backend development, and deployment workflows. Previous teaching, training, or content creation experience is preferred. Excellent communication and presentation skills. Preferred Skills: Experience with cloud deployment and scalable applications. Knowledge of evaluation frameworks and performance optimization techniques. Familiarity with data pipelines, document processing, and search systems. Experience building production-ready retrieval and knowledge-based applications. If you are passionate about knowledge retrieval systems and enjoy teaching through practical, hands-on projects, we would love to hear from you.