“AI Career Pathways” is designed to guide aspiring AI engineers in finding jobs and building a career. The table above shows Workera’s key findings about AI roles and the tasks they perform. You’ll find more insights like this in the free PDF.
From the report:
People in charge of data engineering need strong coding and software
engineering skills, ideally combined with machine learning skills to help them
make good design decisions related to data. Most of the time, data engineering is done using database query languages such as SQL and object-oriented programming languages such as Python, C++, and Java. Big data tools such as Hadoop and Hive are also commonly used.
Modeling is usually programmed in Python, R, Matlab, C++, Java, or another language. It requires strong foundations in mathematics, data science, and machine learning. Deep learning skills are required by some organizations, especially those focusing on computer vision, natural language processing, or speech recognition.
People working in deployment need to write production code, possess strong back-end engineering skills (in Python, Java, C++, and the like), and understand cloud technologies (for example AWS, GCP, and Azure).
Team members working on business analysis need an understanding of
mathematics and data science for analytics, as well as strong communication skills and business acumen. They sometimes use programming languages suchas R, Python, and Tableau, although many tasks can be carried out in a spreadsheet, PowerPoint or Keynote, or an A/B testing software.
Working on AI infrastructure requires broad software engineering skills to write production code and understand cloud technologies.
