  Artificial Intelligence (AI) / Machine Learning (ML)-based systems are widely
sought-after commercial solutions that can automate and augment core business
services. Intelligent systems can improve the quality of services offered and
support scalability through automation. In this paper we describe our
experience in engineering an exploratory system for assessing the quality of
essays supplied by customers of a specialized recruitment support service. The
problem domain is challenging because the open-ended customer-supplied source
text has considerable scope for ambiguity and error, making models for analysis
hard to build. There is also a need to incorporate specialized business domain
knowledge into the intelligent processing systems. To address these challenges,
we experimented with and exploited a number of cloud-based machine learning
models and composed them into an application-specific processing pipeline. This
design allows for modification of the underlying algorithms as more data and
improved techniques become available. We describe our design, and the main
challenges we faced, namely keeping a check on the quality control of the
models, testing the software and deploying the computationally expensive ML
models on the cloud.
