This work presents the search architecture of Amazon Music, which is a highly efficient system designed to retrieve relevant content for users. The architecture consists of three key stages: indexing, retrieval, and ranking. During the indexing stage, data is meticulously parsed and processed to create a comprehensive index that contains dense representations and essential information about each document (such as a music or podcast entity) in the collection, including its title, metadata, and relevant attributes. This indexing process enables fast and efficient data access during retrieval. The retrieval stage utilizes multi-faceted retrieval strategies, resulting in improved identification of candidate matches compared to traditional structured search methods. Subsequently, candidates are ranked based on their relevance to the customer’s query, taking into account document features and personalized factors. With a specific focus on the podcast use case, this paper highlights the deployment of the architecture and demonstrates its effectiveness in enhancing podcast search capabilities, providing tailored and engaging content experiences.