How to become an AI engineer

Artificial intelligence (AI) has made the leap from the big screen into our daily lives. From facial recognition technology to ride-sharing apps and digital intelligent assistants like Siri, AI is now used in almost every corner of our daily lives.

As these technologies evolve and society adopts new AI technologies, the field will only continue to grow, which means there are likely to be plenty of jobs for anyone interested in this field to apply for. And it's not just the job prospects that are enticing. Working in the field of artificial intelligence means you'll be helping to develop cutting-edge technology that directly impacts people and businesses on a daily basis. Not to mention, AI engineers in the U.S. earn a comfortable average salary of $164,769, according to data from ZipRecruiter.

If you're interested in a career as an AI engineer, here's advice on how to get started, as well as tips on how to land your first AI engineering job.

What does an AI engineer do?

AI engineers have specific skills that enable them to develop new technologies, tools and systems that use AI to solve complex problems. For example, an AI engineer could work on a project that uses natural language processing (NLP) to improve the integration of intelligent assistants into our daily lives, or they could focus on reducing the costs associated with product development to help their employer increase profits.

There are several sub-areas of AI, and as an AI engineer, you can choose an area to focus your work on. These include machine learning, deep learning, robotics, machine vision, NLP and speech recognition. You can read more about the role of an AI engineer here.

What skills do you need as an AI engineer?

To become a successful AI engineer, you will need to acquire a variety of technical and soft skills. As AI models are created using algorithms, understanding data structures and algorithms is an essential skill that you will also need to develop. While you may want to specialize in a niche area of AI, for which you will likely need further education and training, you should still understand the basic concepts in these core areas.

Some of the most popular programming languages in the field of AI are Python, Java, R and C++, so mastering one or more of these languages will help your career in AI. Python is particularly popular because its libraries optimize the AI development process and because it is the top choice for NLP. Java is a top language for machine learning, a subset of the AI field, and also a popular language for mobile app development. If you want to focus on statistical software development, data analysis and/or data visualization in the field of AI, then learning R would be a good option for you. And C++ is known for its very high processing speed, which is essential for the performance of complex machine learning models.

In terms of math skills, a solid knowledge of linear algebra, probability and statistics will help you create and use algorithms and interact with common machine learning models, including Hidden Markov, Gaussian Mixture and Naive Bayesian models.

While learning new programming languages and math skills to grow in your job role, you should also focus on developing your soft skills. These skills are more difficult to quantify, but they are critical to your success in any technical role.

Soft skills that AI engineers need include collaboration, problem solving, communication, leadership, time management and understanding how higher-level goals affect outcomes. These skills will allow you to communicate your ideas and solutions with your team and help you be a better team member.

Do I need a degree in AI to become an AI engineer?

If you are looking for AI jobs, you will most likely see a Bachelor's degree as a minimum requirement. However, that doesn't necessarily mean you need a specific AI degree. Since AI is a relatively new field, there aren't many colleges and universities that offer an AI degree.

What recruiters are looking for is a formal education in a related field. This could be computer science, data science, IT, statistics or math. You can also list additional AI-related courses you've taken in college or online that have supported your learning.

Prepare yourself for the job search

Once you have the skills you need as an AI engineer, it's time to start your job search. Hiring managers typically expect a resume that highlights both your technical and soft skills. Read this advice on how to create a technical resume that gets you noticed, as well as these tips on how to show off your soft skills.

Remember to include keywords related to AI in your resume. Some keywords to consider including if they apply to you are the names of programming languages (such as Java, C++, Python and R), computer vision, machine learning algorithms, machine learning libraries, robotics, data science, dynamic programming, statistics and probability.

You may also want to create a portfolio showcasing your best work to include with your applications. You could use your Github or create a personal website for your portfolio. Remember: your portfolio is an added bonus during the hiring process. So look for AI-related projects and skills you can showcase that will give the hiring team extra insight into why you're the best choice for the job.

Prepare yourself for the interview

When interviewing for an AI engineer position, you can expect to be asked both technical and behavioral questions. The interview often begins with a phone screening where you will be asked general questions about your interest in the position as well as clarifying questions about the information on your resume. You should also have the opportunity to ask the recruiter general questions. If the telephone screening goes well, the next step is usually a technical interview.

In a technical interview, you will usually be presented with a difficult scenario and asked how you would solve it. The hiring teams want to know how you would approach the problem and use your skills to solve it. In the field of artificial intelligence, it's all about the problem-solving process. That's why it's important to explain why you would make certain decisions and how you would implement your solution. Don't hesitate to provide the additional details that support your decisions.

It's also a good idea to have a few examples from your previous work that you can talk about in the interview. Ideally, these examples will include AI-related work so you can further illustrate how your skills will benefit the team. Spend some time memorizing key details from these examples so you're prepared to explain them during your interview.