AI Engineer Career Overview & Outlook

Artificial intelligence (AI) has almost endless potential to enhance and simplify human tasks, including image processing, speech recognition, disease diagnosis, business process management, and more.

So it’s no surprise that AI engineers are always in high demand. It is known for plenty of growth potential and an excellent salary to match. This field always welcomes you if you love engineering and have a good background in software programming.

Job Description

The artificial intelligence engineer refers to experts who use AI and machine learning (ML) techniques to build systems and applications that help organizations and companies cut costs, increase efficiency, boost profits, and work better.

What Is AI Engineering?

AI engineering often involves developing the systems, tools, or processes that allow applying artificial intelligence in real life.

All applications where machines mimic human tasks, such as learning and solving issues, can be regarded as AI. Algorithms are “trained” by data, helping them to learn and work better.

AI engineering has been applied to Netflix, YouTube, and Spotify, where machine learning will customize recommendations based on user behavior. AI is also present in transportation, where it drives self-driving cars.

AI engineering is also beneficial in the healthcare industry as it can specify robots to do surgery and make automated image diagnoses.

Artificial intelligence (AI)

Duties and Responsibilities

AI engineers build AI models utilizing ML learning algorithms and deep learning neural networks to gain business insights, which can be applied to develop business decisions that impact the whole organization.

These professionals also make strong or weak AIs, depending on what purposes they want to reach.

AI engineers need a deep understanding of software engineering, data science, and programming. They apply various techniques and tools that allow them to process data and develop and maintain their AI systems.

AI experts can build various kinds of AI applications, such as contextual advertising, language translation, and visual perception or identification.

Generally, an AI engineer plays a crucial role in organizations that utilize AI. The following are some specific responsibilities and tasks of these experts:

  • Develop and manage the artificial intelligence development & production infrastructure.
  • Perform statistical analysis before interpreting the results to optimize the decision-making process of organizations.
  • Automate the AI infrastructure for data science teams.
  • Build the AI model from scratch and support product managers or other stakeholders with implementation and analysis.
  • Collaborate across many teams to help with AI adoption.
  • Change the machine learning model into an API that people can integrate with other applications.

Where Do AI Engineers Work?

An AI engineer commonly works for tech companies, such as Meta, Google, and IBM, and among others, helps them to enhance their software, operations, products, and delivery.

Moreover, they can also be present in research facilities and the government to work to enhance public services.

As you can see, there are almost no opportunities to work from home or remotely for AI engineers.

Education Requirements

You will need at least a bachelor’s degree to become an AI engineer

Secondary School Diploma

The first degree you will need to earn is a high school diploma related to one scientific discipline like physics, chemistry, or mathematics.

Leaving high school with a solid background in scientific subjects will give you a strong foundation to build your later learning.

Bachelor’s Degree

You might consider pursuing a B.Sc (Bachelor of Science) program that will last three years or a B.Tech (Bachelor of Technology) program that will last four years.

You also have the option to gain one engineering degree in a conceptually comparable area, like computer science or information technology.

Then, you can specialize in the AI field with machine learning and data science.

Master’s Degree

You should consider obtaining a Master’s degree that can give you a competing opportunity for an AI engineering career and grow your earning capacity. It also allows you to understand the specialized topic and technical abilities in-depth.


Certification is optional but recommended as it gives you an edge when applying for jobs. The truth is that employers love candidates with certification.

Essential Skills

AI engineers need both hard and soft skills to get the job done

Individuals who want to become an AI engineer should know about the required skills, including:

Soft Skills

1. Critical Thinking Ability

As an AI engineer, you may have to give various answers and solutions to an issue. You might have to provide facts to build reasonable conclusions quickly.

Although you can get and strengthen these capabilities while earning a bachelor’s degree, exploring additional experiences and expanding your talents in the area is possible if needed.

2. Business Intelligence

In most cases, successful AI initiatives can solve issues related to an organization’s management. So if you own business intelligence, it is easy to convert your technological ideas into effective commercial ventures.

You can establish a basic foundation of how companies and organizations function, the audiences they aim for, and the competition between them, regardless of the field in which you work.

3. Teamwork

As an AI engineer, you are usually part of a team, meaning you must work with many other professionals. So the ability to operate productively and successfully in one team is valuable to you.

While working, you may have to work with small and large groups to accomplish many complex objectives. So you will need to listen to other members’ opinions and give your own opinions briefly and clearly through communication, helping you become a successful part of your team.

Time Management

Artificial intelligence engineers need to balance the needs of stakeholders and the need to do research, plan and organize projects, create software, and check it. So, managing time is crucial to becoming an effective team member.

4. Domain Expertise

These engineers must recognize the demands of their companies and the barriers their designs are addressing to develop optimized solutions and self-running programs used by their clients and organizations.

Lacking expertise in relevant fields might cause inaccurate suggestions, incomplete works, and difficult-to-assess models.

5. Problem-solving Abilities

Successful AI engineers are capable of solving problems and thinking creatively.

The AI field always requires solving issues when they appear in real-time, so the development of problem-solving skills, which are creative and critical, is a must-have.

6. Communication Skills

When you become an engineer in this field, you might have to talk to people with various competence levels.

Suppose you are asked to design and offer a new AI model to a division inside your company. You want to improve your spoken and written communication abilities to convey complex concepts and thoughts to various individuals.

Hard Skills

1. Algorithms and Frameworks

A deep understanding of how the machine learning algorithm works will help you implement the machine learning model more effectively and easily. These algorithms might be linear regression, Naive Bayes, KNN, and Support Vector Machine.

In addition, to build the AI model with unstructured data, these engineers should understand deep learning algorithms and implement them utilizing a framework.

Some of the common frameworks used in AI engineering are Theano, TensorFlow, PyTorch, and Caffe.

2. Spark and Big Data Technologies

AI engineers can work with large loads of data, including real-time or streaming production-level data in petabytes or terabytes. So they need to know about Spark or another big data technology to make sense of such data.

Besides Apache Spark, these engineers can utilize other big data technologies, such as Cassandra, MongoDB, and Hadoop.

3. Probability, Statistics, and Linear Algebra

An in-depth understanding of probability, statistics, and linear algebra is also essential to understanding and implementing various AI models, such as Hidden Markov, Gaussian mixture, and Naive Bayes.

4. Programming Skills

This skill is essential for anyone who wants to become an AI engineer.

To become well-versed in this field, you must learn programming languages, such as R, Java, C++, and Python, to design and implement models.

Essential Tools

The following is the Artificial Intelligence tools list of the most popular tools and frameworks available in the market.

  • Scikit Learn
  • TensorFlow
  • PyTorch
  • CNTK
  • Caffe
  • Apache MXNet
  • Keras
  • OpenNN
  • AutoML
  • H2O

Job Outlook

Positions in the AI field and related IT research & science jobs are predicted to grow by 15% from 2019 to 2029. This speed is much faster than the average expected job growth for all occupations.

These experts are always at the forefront of applying cutting-edge technologies to produce consumer-facing applications that can drive innovations in using technology (like smartphones).

Moreover, they also work on groundbreaking initiatives for private sector industries and government that revolutionize how to get the work done and implications for online security.

In addition, the need for professionals who can interpret data is also increasing when the technology’s ability to collect data is growing. And AI engineers also benefited from this increase.


The average annual salary for an AI engineer in the US is $158,311. By simple calculation, it is easy to know that this average salary is equivalent to $76.11 per hour, $3,044 per week, and $13,192 per month.

The average annual salary of these engineers typically ranges from $133,000 to $170,000, with top earners making approximately $209,000 annually across the US.

Advantages and Disadvantages

Here are some common pros and cons of being an AI engineer:


  • Great work environment.
  • High average salary.
  • Always in high demand.
  • Unleash your creativity.
  • Access to advanced technologies.
  • Useful in many industries.


  • The focus of the position.
  • No opportunity to work remotely.
  • Educational requirements.
  • Pressure and stressful


An AI engineer is a good career for a good reason

Is an AI Engineer a Promising Career?

The short answer is Yes. AI engineering was one of 2023’s top emerging jobs.

With an increasing demand for trained AI professionals, AI engineer job outlook and salaries are as strong as ever.

How to Become an AI Engineer?

Although everyone’s career journey might differ, here are three potential steps to becoming an AI engineer.

Step 1: Gain Necessary Education

Step 2: Develop Technical Skills

Step 3: Choose a Specialization and Earn Certifications

How Long Does It Take To Become an AI Engineer?

It usually takes roughly six months to complete a machine learning engineering curriculum. It can take longer if you start without prior knowledge of data science, computer programming, or statistics.

Career Guidance

AI engineering is a promising career. People with artificial technology skills are forecast to experience high job growth.

It is one of the right paths if you are a natural systems thinker interested in technology, solving complicated problems, and utilizing critical thinking skills.

You should complete your education and get a bachelor’s degree before seeking positions in the AI field. Getting at least one certification is recommended as it will help you stand out from other candidates and be favored by employers.

Some jobs in this field may require a higher degree, such as a master’s degree (or even a doctorate). So you should know your career goals early to prepare well for your education.