Automated Machine Learning (AutoML) is an upcoming field in machine learning (ML) that searches the candidate model space for a given task, dataset and an evaluation metric and returns the best performing model on the supplied dataset as per the given metric. AutoML not only reduces the man-power and expertise needed to develop ML models but also decreases the time-to-market for ML models substantially. In Walmart, we have designed an enterprise-scale AutoML frame-work called WALTS to meet the rising demand of employing ML in the retail business, and thus help democratize ML within our organization. In this work, we delve into the design of WALTS from both algorithmic and architectural perspectives. Specfiically, we elaborate on how we explore models from a pool of candidates along with describing our choice of technology stack to make the whole process scalable and robust. We illustrate the process with the help of a business use-case, and finally underline how WALTS has impacted our business so far.