How do AI Engineers and Data Scientists Work Together?


The business realm is significantly being shaped by technology progressing at as rocket fast speed. From Artificial Intelligence to data science to machine learning, new technologies are making waves all around creating a curiosity to know more about them. A frequently discussed topic creating eagerness amongst people is the role of AI software Engineers.

If you surf the web and explore any of the job sites, you will see that a plethora of postings display a requirement for AI software engineers which shows that this is a trending job role and it is worth to have a conversation over it. So, here I am writing a whole article over it. Read on to widen your knowledge horizons of this new position in the job market.

The key function of an AI Engineer is to enable the data science team to serve its customers or company stakeholders by productizing their work. Moreover, if they are a part of an organization, they can definitely not work in isolation. In other words, they have to bond and coordinate with the company’s business analysts, data architects and data scientists in the pursuance of ensuring that the business objectives are met effectively.

AI engineer and data scientist work hand in hand for the development of an organization. Further, it is an obvious responsibility if engineers in Artificial Intelligence to keep tabs on the latest breakthroughs in their industry in order to bring higher evolutions in the business and enhance the customer experience. Cutting long story short, these AI ninjas work towards amalgamating software engineering with data science to attract business success.

Build Infrastructure as Code

Mechanization of Data Science team’s infrastructure is an important task in the hands of AI engineers since a data science project cannot succeed without it. They ensure that the surroundings contrived at the time of model development as well as training are manageable and replicable for the concluding product.

Creating a self-contained and transportable environment needs knowledge of tools like Docker, Vagrant and Anaconda for making sure that models are conveniently deployable and a strong alliance is maintained amongst data science teams and all of this orchestrated by AI engineers. But, that certainly does not mean that the job of engineers in Artificial Intelligence is limited to assisting data scientist when they are focusing on model development.

Continuous Integration along with Versioning Control

This is another aspect of data science projects which can only be addressed by the Ai engineers. They have to deploy tools like GIT or TFS in the day to day procedures if data science ventures. Also, the responsibility of reducing repetitions, keeping track of disparate updates and ensuring a proper control system in the correct place is also on the shoulders of these professionals.

API Creation

Another area where the presence of AI engineers is unavoidable is where APIs are developed for integrating source and data products into applications. A part of their job is to build as well as maintain platforms for converting models into application program interfaces which are consumable by various applications. Therefore, they have to be well-versed with the common language and standard approach for developing tools and APIs.

This can be seen as a basic sketch of what AI Engineers do in data science teams and what they bring to the table when it comes to fulfilling business goals. Hopefully, this must have given you a clearer idea about what it takes to become an engineer in the AI realm and how are they helpful to data scientists.