• Design, build, and manage end-to-end RAG pipelines for enterprise AI applications. • Lead preprocessing of unstructured data, including discovery, classification, cleansing, redaction, and metadata enrichment. • Develop and optimize document chunking, embedding, and vectorization strategies for structured and unstructured datasets. • Coordinate ingestion of curated datasets into vector databases and AI platforms. • Package curated unstructured datasets as governed, reusable data products for enterprise consumption. • Define and implement metadata tagging strategies to align with Collibra governance standards. • Partner with Data Governance and Data Quality teams to ensure AI-ready data meets enterprise standards for lineage, classification, and compliance. • Evaluate and optimize embedding models, retrieval strategies, and indexing performance. • Monitor and tune RAG pipeline performance, including latency, retrieval accuracy, and cost efficiency. • Implement automation for document ingestion, transformation, and publishing workflows. • Support integration with enterprise AI platforms (e.g., ChatGPT Enterprise, AskSage, Moveworks). • Conduct cost analysis and capacity planning for vector storage and processing workloads. • Provide technical guidance on AI data readiness and unstructured data lifecycle management. • Design, implement, and optimize enterprise-grade RAG and prompt engineering frameworks, including context engineering strategies (chunking, metadata enrichment, semantic filtering, dynamic context management) to improve retrieval accuracy, grounding, and response quality. • Develop and maintain scalable multi-modal data pipelines that ingest, preprocess, embed, and integrate text, documents, images, audio, and structured data into governed vectorized data products consumable by enterprise AI platforms.