Query understanding is a critical component of e-commerce platforms, enabling accurate interpretation of users’ intents and efficient retrieval of relevant products. This paper presents a study on scalable query understanding techniques applied to a real use case in the e-commerce grocery domain. We propose a novel architecture that combines deep learning models with traditional ML models to capture query nuances and provide robust performance. Our model ensemble approach aims to capture the nuances of user queries and provide robust performance across various query types and categories. We conduct experiments on real-life datasets and demonstrate the effectiveness of our proposed solution in terms of accuracy and scalability. An optimized graph-based architecture using Ray enables efficient processing of high-volume traffic. The experimental results highlight the benefits of combining diverse models.