Levi Strauss is looking to build an in-house recommendation engine that can handle compute intensive functions, partially render responses to reduce latency, and ensure high availability. The engine would need to provide real-time contextual recommendations. Dhivya Rajprasad, a data scientist at Levi Strauss, is interested in the requirements for such a system.