Roles of an AI team



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Some AI products may require a large group of AI teams, sometimes more than 100 to build a product. In this article, I would share the different roles and responsibilities of a large AI team. It can help to understand, what are the products needed to build a complex AI product.

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As AI is evolving really fast, so the job titles are not 100% defined and there can be a difference in the name of the roles used in various companies for AI teams. Most AI teams consist of the roles which are discussed below-

Software Engineer —
Many AI teams consist of Software Engineers. For example — in smart speakers, we need software engineers to write specialized software for joke execution, set a timer, or answer a question about today’s weather. These are traditional software engineering tasks. Ensuring the reliability of self-driving cars, so they would not crash is also a role of a software engineer in an AI team. It is pretty common to have AI teams with a large fraction of software engineers sometimes 50 percent and sometimes even more than that.

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Machine Learning Engineer —
Machine Learning Engineer writes the software responsible for generating the A to B mapping or for building other machine learning algorithms needed for the product. So, they can gather the data of photos of cars and positions of bikes, train a neural network or a deep learning algorithm and work iteratively to make sure that the learning algorithm is giving accurate and efficient outputs.

Machine Learning Researcher—
The role of machine learning researchers is to extend the state of the art in machine learning. Machine learning and Artificial Intelligence are still advancing rapidly. So, many companies for-profit and non-profit may have Machine Learning Researchers responsible for extending the research of machine learning. Some Machine Learning Researchers also publish papers, but many companies also have Machine Learning Researchers that do research but are less focused on publishing.

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Applied machine learning Scientist —
This is the job in an AI team that live between machine learning engineer and machine learning researcher. Machine learning scientist does the work of both. They are often responsible for going to the academic literature or the research literature and finding the steady techniques and finding ways to adapt them to the problem they are facing such as how to take the most cutting edge algorithms where the normal algorithm is not giving efficient output and adapt that to the model or project they are working on.

Data Scientist —
The role of a data scientist is not really well defined and the meaning is still evolving day by day. The primary responsibilities of Data Scientists are to examine data and provide insights, as well as to make presentations to teams or executives in order to help drive business decision-making much easier and beneficial for the company.
There are many data scientists which are doing other tasks also. As the field of big data and machine learning is evolving tremendously, so the work of a data scientist sometimes matches with a machine learning engineer.

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Data Engineer —
The main role which comes under data engineer is to organize data, meaning, to make sure that your data is saved and is easily accessible, safe, secure, and in a cost-effective way.
Saving data is very crucial for a data engineer. It can make the work of an AI team much effective and quick. So, self-driving cars generate a lot of data, and saving the data for many days or weeks or months, or years of operation starts to require serious data engineering from a data engineer.

AI Product Manager —
They are the ones whose job is to decide which and what product to build, in other words, what is more, feasible and valuable. The job of a traditional product manager was already to decide what to build as well as sometimes some other roles, but the AI product manager now has to do this in the AI era and they need new skill sets to figure out what’s feasible and valuable in light of what AI can and cannot do today.

As the field of AI is still evolving, so none of these job titles are completely specified and different companies will use the job titles somewhat differently.

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