Introduction

With the surge in in-house AI and machine learning skills, there are a variety of career options for those looking to enter the market, including AI architects and BI developers. AI projects often fail due to lack of critical internal skills and lack of data scientists. As companies incorporate AI, natural language processing and machine learning into their products and services, the need for skills and talent forces them to fill the respective positions. 

As a result, job seekers are finding an explosive labor market for data science skills. Companies are also hiring artificial intelligence specialists. However, the skill gap is widening as the need for these roles continues to outweigh the supply. By the end of 2021, an estimated 2.3 million jobs will be created in AI-related areas worldwide. However, there are many more important roles to be filled by IT professionals in particular in the areas of data science, business analytics and mathematics. This article discusses 4 upcoming career paths for IT professionals in AI. 

Data Scientist

A data scientist is responsible for collecting and analyzing data. Data scientists should have a background in advanced mathematics and statistics, advanced analytics,  machine learning and AI. In a corporate environment, data scientists extract useful information from large amounts of data. They analyze the data, draw conclusions, gather insights and use them to help the company. Over the last five years, the need for data scientists has increased by 35% globally. This sudden surge in demand for data scientists has caused the talent crisis witnessed by many companies and organizations. 

The requirements to be a data scientist are straightforward. A computer science degree and experience in coding will do. This being the case, the role of a data scientist is loosely defined and job descriptions vary across organizations. Ideally, you’d have a background in statistics, algorithms, probability and mathematics. 

Machine Learning Engineer

Machine learning engineers are software engineers who specialize in machine learning. This role helps to establish the machine learning process, use machine learning in production environments and optimize the product in terms of performance and scalability. The main task of a machine learning engineer is to develop the various tools needed to train, generate, evaluate, iterate and manage machine learning models in the training and inference phases. 

Data Scientists focus primarily on the data and environment for managing the data in the model for a variety of purposes, but the challenge of generating machine learning models is particularly limiting. Data scientists can act as machine learning engineers, but it is much more difficult for a dedicated machine learning engineer to become a data scientist without more general knowledge of data science. 

Machine learning engineers need to have knowledge of computer science, statistics, various programming languages, machine learning, deep learning, applied mathematics and other skills related to engineering and AI. In general, machine learning engineers need to have a broad understanding of various other parts of AI. Additionally, machine learning engineers need to be familiar with the various tools available in the life cycle of machine learning models to keep up  with the changing landscape of AI vendors.

Business Intelligence Developer

A fairly vague role that is becoming more and more visible is the role of  business intelligence developers. BI developers start with the roots of business intelligence (BI) software and focus on developing stunning views of  data  that  business decision makers can use to  better understand key problem areas. BI developers primarily use  software tools focused on BI, portals, dashboards and data analysis software suites and use these tools to summarize dashboards, reports, graphs, maps, charts and other data visualizations. 

Business intelligence developers use software tools to transform data into useful insights that support business decisions. This role is a combination of  business  and AI professionals. One of the main roles of a business intelligence developer is to capture and decompose data  to generate business insights. You need to understand BI tools that help you access and analyze datasets, present your analysis results, and  provide detailed reports. 

Business intelligence developers need to understand business requirements. You also need knowledge of SQL and relational databases, knowledge of BI and programming experience. As the term “business intelligence developer” becomes more and more important, this role could become better known in the long run as a data visualization engineer or data user experience supervisor. 

AI Architect

The role of an AI architect is different from that of a machine learning engineer or data scientist. Knowledge businesses are keen to hire AI architects in addition to the other roles. An AI ​​architect is responsible for the overall requirements of the artificial intelligence project. This role is responsible for building and maintaining the architecture using leading AI technology frameworks. This role integrates aspects of data science, solution specialists and technology experts into one position. 

In many ways, AI architects are just as important to AI projects as enterprise architects are to IT projects. Enterprise architects use best-practice methods to play a special role in focusing on overall strategy, planning and coordination. AI architects also use new AI-oriented methods to focus on overall strategy, planning and coordination. 

AI Architects can see the big picture of AI deployment projects, understand the goals of the overall mission, how to apply AI to those goals and eventually fulfill them. They need to be able to coordinate a team, as well as understand how AI is used in organizations. This requires a deep understanding of different AI patterns, the capabilities of AI ​​platforms and the state of the data in your organization. Due to these requirements, AI architects require a degree of engineering experience and are not considered as entry-level positions. 

Conclusion

Over the past few years, the AI ​​job market has grown tremendously and the demand for AI specialist jobs has skyrocketed. When looking at these high-demand jobs, you need to consider the core competencies and requirements of each job listing. Some of these roles require in-depth knowledge of specific technologies such as deep learning, predictive models, natural language processing as well as knowledge of mathematics and engineering or years of business experience. 

It’s not easy to master these skills over the weekend or train yourself on your side. If you are interested in learning new skills, many universities  offer courses in machine learning, deep learning and other AI technologies. Knowing the most needed positions will help you prepare to enter the industry and plan to advance your career.