Artificial intelligence (AI) is a booming field with an amazing potential for job growth.
You’ve no doubt seen AI technology all over the news as of late – the launch of ChatGPT and AI-powered search engines, for example. If you have been studying artificial intelligence, machine learning, and computer science, this is great news for your career. Why?
This astounding technology needs an army of competent professionals to guide its growth and expand the boundaries of what it can do. Below, we’ll discuss some of the jobs you can get by studying artificial intelligence.
One thing is for sure, though – you can’t ask ChatGPT to write a resume for you. If you want good results in your job search, you will need to give your resume a personalized, human touch. Check out this machine learning engineer resume for a bit of inspiration.
AI Job Outlooks
Artificial intelligence is a rapidly growing field. According to Analytics Insight magazine, “the number of job postings with AI or machine learning in the title is doubling year by year.”
In fact, there are currently more available openings in AI than there are qualified candidates. The U.S. Bureau of Labor Statistics predicts that this trend will continue in the near future, with a 31.4 percent increase in AI jobs by 2030.
This is great news if you are looking for a job in AI – there is less competition than in other fields. If you have the training and experience, you are likely to get hired.
UpGrad reports that big-name companies like Amazon, Apple, Google, and Meta are all currently recruiting AI professionals. But you shouldn’t limit your job search to these household names. Why not?
AI has applications in such diverse fields as healthcare, education, sports, agriculture, construction, banking, marketing, and e-commerce, so scores of smaller and less familiar companies will be seeking AI experts as well.
Now, let’s take a closer look at some promising AI job titles.
Algorithm Engineer/Algorithm Specialist
Algorithm engineers optimize existing algorithms and design new ones to solve business dilemmas. They must be fluent in multiple programming languages.
Artificial Intelligence Engineer
AI engineers are problem-solvers. They use different AI models to develop, test, and apply solutions to business problems.
Business Intelligence Developer
Business Intelligence developers feed data to AI systems to create simulations that inform business decisions. In other words, they help decision-makers make sense of huge amounts of data by giving it real-world application.
Computer scientists solve computing problems, improve existing systems, and create new products or devices by conceptualizing mathematical and computational issues.
Computer Vision Engineer
Some machines need to be able to “see” in order to do their jobs. Computer vision engineers enable this via image and object recognition systems, using cameras and other equipment.
Data engineers are tasked with preparing data for use by data scientists. They “collect, manage, and convert raw data into usable information for data scientists and business analysts to interpret.” Their goal is to make the data accessible to other users.
Data scientists extract “value” from data – in other words, they analyze and interpret data to gain business insights. This may involve creating machine learning tools that automate the data analysis process. Analysts then review the data “to identify key insights into a business’s customers and ways the data can be used to solve problems. They also communicate this information to company leadership and other stakeholders.”
Director Of Analytics
Analytics directors oversee data-related departments and apply data analytics techniques to support the objectives of the business.
Machine Learning Engineer/Researcher
Machine learning engineers “create computer programs that perform defined tasks without any specific programming,” “building machine learning algorithms that solve business problems.”
Mechanical engineers study physical machines, force, and movement. In the AI field, these physical aspects are combined with AI programming to create machines that learn.
This leadership role requires the data scientist to act as a technical consultant to various departments within a company.
Product managers identify consumer needs and business goals that a product can meet. Today, product managers use AI to help “strategically collect data” to meet these objectives.
Research scientists do just that – research. In this case, they focus their efforts on the frontiers of machine learning.
AI isn’t limited to information alone. Robots can also be trained to carry out specific tasks. AI will help them to ‘teach themselves’ to overcome new challenges.
Artificial intelligence is a growing field with a lot of potential. If you’ve studied AI, you might consider a career as a machine learning engineer in any of the above promising fields.