The exponential growth of video data presents significant challenges for effective content discovery. Traditional keyword search falls short when dealing with visual nuances. This talk addresses the design and implementation of a robust system for large-scale, multi-modal video retrieval, enabling search across petabytes of data using diverse inputs like text descriptions (e.g., appearance, actions) and query images (e.g., faces). We will explore an architecture combining efficient batch preprocessing for feature extraction (including person detection, face/CLIP-style embeddings) with optimized vector database indexing. Attendees will learn about strategies for managing massive datasets, optimizing ML inference pipelines for speed and cost-efficiency (touching upon lightweight models and specialized runtimes), and building interactive systems that bridge pre-computed indexes with real-time analysis capabilities for enhanced insights.