The main role of an AI Software Engineer in a Data Science team is to productize the data science work so it can serve an internal stakeholder or external customers. The AI Engineer must collaborate with the Data Scientists, Data Architects and Business Analysts to ensure alignment between the business objectives and the analytics back end.
And, to justify the AI portion in the job title, an AI Software Engineer is responsible for staying up to date and informed about breakthrough artificial intelligence technologies with the potential to transform business, the workforce or consumer experience and how that can be leveraged by the Data Science team.
It means, to put it in a simple statement, that the AI Engineer is responsible for bringing a Software Engineering culture into the Data Science process. That is a massive task and involves things like:
Build Infrastructure as Code
Automatization of the Data Science team infrastructure. This important Software Engineering concept is a key part of a successful Data Science project. The AI Software Engineer is responsible for making sure that the environments created during the model development and training can be easily managed and replicated for the final product.
Continuous Integration and Versioning Control
This is another important fact for a Software Engineer that can be easily missed in a Data Science team. Tools such as TFS or GIT should be part of the daily process of a Data Science project. During the model development, there are so many iterations and different updates that is impossible to keep track of all that has been done without a proper versioning control system in place.
Tests
Any product, that being a model with a simple user interface or a fully integrated application, should be thoroughly tested. Obviously, from a Software Engineer point of view, those tests should be fully automated.
API Development
Development of APIs to help integrate data products and source into applications. The AI Software Engineer is responsible for build and maintain a platform to easily "convert" the models into APIs that can be consumed by other applications. That means the development of tools or custom APIs that follow a standard approach and a common language. That also means that the Data Science team can quickly spin a model into an API that is consumed by the "outside world".
Read Full Article @ https://towardsdatascience.com/what-is-the-role-of-an-ai-software-engineer-in-a-data-science-team-eec987203ceb
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