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How To Become An AI Engineer

Artificial intelligence (AI) has jumped off the movie screen and into our everyday lives. From facial recognition technology to ride-sharing apps to digital smart assistants like Siri, AI is now used in nearly every corner of our daily lives.

As these technologies advance and society adopts new technologies that use AI, the field is only going to continue to grow, which means there will likely be plenty of jobs to apply to for anyone interested in getting into this field. And it’s not just the job outlook that’s appealing. Working in AI means you’ll support the development of cutting-edge technology that directly impacts people and businesses on a daily basis. Not to mention, in the U.S., AI Engineers earn a comfortable average salary of $164,769, according to data from ZipRecruiter.

If you’re interested in a career in AI engineering, here’s advice on how to get started, plus tips on how to land your first AI Engineer role.

What does an AI Engineer do?

AI Engineers have specific skills that allow them to develop new technologies, tools, and systems that use AI to solve complex problems. For example, an AI Engineer might work on a project that uses natural language processing (NLP) to improve the integration of smart assistants in our daily lives, or they may be focused on decreasing the costs associated with product development to help their employer boost profits.

There are several subsets of AI, and as an AI Engineer, you may 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 to be an AI Engineer?

To be a successful AI Engineer, you’ll need to gain a variety of technical skills and soft skills. Your technical skills will span programming languages, data science, and mathematics; and since AI models are built using algorithms, understanding data structures and algorithms will be an essential skill you’ll need to develop as well. Although you may decide to specialize in a niche area of AI, which will likely require further education and training, you’ll still want to understand the basic concepts in these core areas.

Some of the most popular programming languages in AI are Python, Java, R, and C++, so mastering one or more of these languages will support your career in AI. Python is particularly popular because its libraries are designed to optimize the AI development process, and because it’s a top choice for NLP. Java is a top language for machine learning, a subset of the AI field, and it’s a go-to language for mobile app development as well.

R is commonly used for statistical software development, data analysis, and/or data visualization in AI. And C++ is known for its very fast processing speed, which is essential to the performance of complex machine learning models.

As far as math skills go, a solid understanding of linear algebra, probability, and statistics will help you create and use algorithms, as well as interact with common machine learning models, including Hidden Markov, Gaussian mixture, and Naive Bayes models.

While you’re learning new programming languages and mathematical skills to grow in your professional role, you’ll also want to focus on developing your soft skills. These skills are harder to quantify, but they’ll be crucial to your success in any technical role.

Some of the soft skills that AI Engineers need include collaboration, problem-solving, communication, leadership, time management, and understanding how high-level objectives influence outcomes. These skills will enable you to communicate your ideas and solutions with your team, and also help you be a better team member.

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

When you’re researching jobs in AI, you’ll most likely see a minimum education requirement of a Bachelor’s Degree. But this doesn’t necessarily mean that you need a specific AI degree. In fact, since AI is a relatively new field, there aren’t that many colleges and universities that offer AI degrees in the first place.

What hiring managers are looking for is some formal education in a related field. This could be computer science, data science, IT, statistics, or math. And then you can highlight any additional courses related to AI that you took in college or online that supported your learning.

Once you have the skills you’ll need to become an AI Engineer, it’s time to begin your job search. Hiring managers will generally expect to see a resume highlighting your technical skills, as well as your soft skills. Check out this advice on how to create a technical resume that’ll get you noticed, as well as these tips on how to showcase your soft skills.

Remember to include keywords related to AI on your resume. Some keywords you should 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 that highlights your best work to include with your applications. You could use your Github or create a personal website for your portfolio. Here are some tips on how to use Github to strengthen your resume. Remember: Your portfolio is an added bonus during the hiring process, so look for AI-related projects and skills to present that will give the hiring team extra insight into why you’re the best fit for the job.

Prepare for the interview

When interviewing for AI Engineer roles, you can expect to be asked both technical and behavioral interview questions. The interview process often kicks off with a phone screening where you’ll be asked general questions about your interest in the position, as well as any clarifying questions related to the information on your resume. You should also be given time to ask any general questions you have for the recruiter. If the phone screening goes well, the next step is usually a technical interview.

During a technical interview, it’s common to be given a challenging scenario and asked how you’d solve it. Hiring teams want to hear how you’d approach the problem and apply your skills to begin to solve it. In the AI field, it’s all about the problem-solving process, so explaining why you’re making certain decisions and how you’d go about implementing your solution is crucial. Don’t hesitate to give those extra details that back up your decisions.

It’s also a good idea to have a few examples from your past work that you can talk about during your interview. Ideally, these examples would include AI-related work so you can further highlight how your skill set will benefit their team. Spend some time memorizing important details from these examples so you’re prepared to talk through them during your interview.

Looking for more interview advice? Here are more tips on how to ace your technical and behavioral interview. You can also find more resume, portfolio, and interview tips at our Career Center.

Getting started in AI

With new research and daily advancements in technology, there’s always something new to learn in the ever-changing field of artificial intelligence. Whether you’re looking to learn a new software library for machine learning or a new programming language to support your work, our courses can help.

Looking for machine learning-related courses? Check out Learn the Basics of Machine Learning, Build a Machine Learning Model with Python, or Build Deep Learning Models with TensorFlow. If you’re interested in learning a new programming language, take a look at Learn Python, Learn R, Learn Java, and Learn C++, plus many more in our course catalog. We also offer courses on Probability and Linear Algebra.


AI Courses & Tutorials | Codecademy
Artificial intelligence (AI) uses computers and other machines to accomplish complicated tasks typically associated with the human mind — like problem-solving and decision-making.
Machine Learning Courses & Tutorials | Codecademy
Machine Learning is an increasingly hot field of data science dedicated to enabling computers to learn from data. From spam filtering in social networks to computer vision for self-driving cars, the potential applications of Machine Learning are vast.

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Adam Carpenter

Adam Carpenter

Adam Carpenter is a tech, fintech, and business innovations writer. Passionate about user safety, Adam writes about cybersecurity solutions, software, and innovations.

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How To Become An AI Engineer
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