AI Data Architect (Remote)

RAG System Design:

• Design end-to-end Retrieval-Augmented Generation (RAG) architecture, including ingestion, chunking, embedding, indexing, retrieval, and response generation
• Define chunking strategies based on content type, semantic coherence, and use case requirements
• Build metadata schemas, tagging frameworks, and document structures to optimize retrieval precision

Data Structuring & Normalization:

• Own upstream data preparation standards that enable effective retrieval, clearly separating data structuring responsibilities from downstream retrieval and RAG execution
• Define standards for document ingestion, cleaning, parsing, and normalization across structured and unstructured enterprise data prior to retrieval
• Transform raw enterprise data (PDFs, knowledge bases, policies, call transcripts, wiki pages) into AI-ready formats

Business Logic to AI Translation:

• Lead the extraction, structuring, and codification of business domain knowledge for AI consumption
• Translate business rules into metadata models, labeling strategies, and retrieval logic
• Define how different content types (policies, FAQs, procedures, product documentation) are interpreted, prioritized, and surfaced by AI