• Design and implement context pipelines for LLM-based systems. • Structure information to maximize model understanding and response quality. • Define strategies for prompt composition, context injection, and tool usage. • Build and optimize RAG pipelines using vector databases. • Implement document ingestion, chunking, embedding, and retrieval strategies. • Improve retrieval precision and reduce hallucinations in AI outputs. • Design and maintain prompt frameworks for AI agents and applications. • Optimize prompts through systematic testing and evaluation. • Integrate prompts with tool use, APIs, and agent workflows. • Structure knowledge bases for AI consumption. • Implement pipelines for data preprocessing, indexing, and embedding generation. • Manage semantic search and knowledge retrieval systems. • Analyze model performance and improve context efficiency. • Monitor latency, token usage, and system scalability. • Develop evaluation methods to measure prompt and context performance. • Work closely with AI Engineers, Data Engineers, and Product Teams. • Translate business requirements into AI-powered solutions. • Document context architectures and AI workflows.