AI Engineers: Your Guide to What They Are and How to Become One

As a species, we’ve long been obsessed with finding an intelligent existence that would help us survive in this world. We started by domesticating wolves and cattle more than 10,000 years ago. Then, we trained them across multiple generations to help us in hunting and around the farm. Now, we’re trying to create a wholly new subspecies.

Our needs at this age have changed. Instead of cattle skinning, we need to learn Python. It’s a big change, I know! But that’s why I’m writing this post today to show you what skills you’ll need to survive as an AI engineer. I’ll first help you understand what an AI engineer is. Then, I’ll take you through the types of tasks that you can expect to do when you start your job. If you’re still excited about it at the end, I’ll show you how to become one.

What’s an AI Engineer?

Let us first define what an AI engineer is. Now, artificial intelligence is defined as follows by Encyclopedia Britannica: It’s “the ability of a digital computer or computer-controlled robot to perform tasks commonly associated with intelligent beings.”

So you can infer that an AI engineer is someone capable of harnessing and using this ability. Maybe that’s a vague way of putting it, so let me give you some examples.

Research in AI at the moment is working on mimicking human abilities, especially speech and sight. These tasks, although natural for us, are actually difficult for computers. That’s not to say that computers are dumb! It’s just that our brains have been evolving for more than thousands of years, so we can control our bodies perfectly for our needs. We can move using our legs and recognize people or objects on the street. The human brain is a very intricate “machine,” and we haven’t even begun to understand some of its inner workings.

The two main fields of research are as follows:

• Computer vision, in which we use pictures and videos to make computers aware of objects and surroundings.
• Natural language processing, in which we use texts to make computers understand the meaning of words.

You can become a specialist in either of these fields. Although there are some similarities in how researchers approach these problems, there are distinct differences in the technologies used.

But the basis of all these computations is data. For example in computer vision, pictures are our input data. To stretch the Stone Age comparisons, data to AI engineers is like fire to Neanderthals. It allows us to exist, and we often marvel at its beauty. Data is simply the building block of AI.

To summarize, AI engineers use data as an input to make computers achieve a specific task. These tasks can range from differentiating between dogs and cats in pictures to understanding Hamlet. The range of tasks you can make a computer do is unlimited as long as you possess the necessary data.

What Does an AI Engineer Do?

There are multiple things you can find yourself doing as an AI engineer. However, you might find that you don’t create new AI every day. That’s not how this job works!

Requirements

First, let me tell you what you need to know before you start your job:

• Linear algebra: Oh yes! You need every bit of those math lessons that you might have skipped during school.
• Statistical modeling: It’s important to understand statistics because you’ll be interacting with data all the time. You need to be able to describe your data with numbers (such as mean and standard deviation) and understand what each number entails.
• Programming: Because you’re trying to make computers smart, you’ll need to communicate with them. So, learning programming languages is a necessity. Python is the main language for AI. You’ll do well to take some software engineering courses to learn how to write and structure your code.
• Data queries: As I mentioned earlier, you’ll be spending most of your time working with data. You’ll need to learn programming languages that allow you to access the data in the database (such as SQL and Hadoop).

An AI engineer needs to be good at math and programming. Math describes knowledge, and programming allows us to encode this knowledge into the computer. Now, to encode this knowledge, we need to have an understanding of certain computer algorithms. These algorithms apply math to extract the knowledge from the data, and we can invoke them using code.

AI Algorithms

Some of the algorithms that AI engineers rely on include the following:

• Neural networks: These algorithms are inspired by how the neural networks in the human brain function. You can find many types. Each type has its own advantages and use case. It may be a little too early for you to delve into this topic, but I’ll mention convolutional neural networks (CNNs) as an example.
• Decision trees: True to its name, a decision tree is a tree-like graph that outputs an outcome (decision) based on the data.
• Logistic regression: This is a powerful algorithm that classifies data into different categories.

There are many more algorithms that AI engineers use. The common thing all these algorithms share is that they have their roots in math and statistics.

Day-to-Day Work

Most job ads you’ll find don’t actually include the title of “AI Engineer.” You’ll find job ads mainly related to the following:

• Data Scientist: This is definitely a job title that’s very vague. You can find yourself doing anything from data queries to data exploration or applying the algorithms I mentioned earlier.
• Machine Learning Engineer: This position is for engineers who wish to focus more on the algorithmic side. You may find yourself not working on neural networks most of the time, though.
• Deep Learning Engineer: This position is for engineers who are hardcore passionate about neural networks. These algorithms are creating huge hype around AI, and it’s a very active field.

So many fields try to use AI nowadays. You can find two people with the same title but who are doing completely different things because each company has its different take on AI.

So, How Can You Become an AI Engineer?

The field is relatively new, and a lot of talent is pouring in at the moment. A university degree is a good place to start so you can compete against all the people looking to break into the field. A computer science or software engineering degree might be a good start.

There are a lot of resources on the internet—such as massive open online courses (MOOCs) and textbooks—that can help guide you on your first steps. After that, you’ll need to work on some projects to fill in your resume. Getting some initial experience via an internship will help differentiate you from other candidates.

Many recruiters prefer people with high education levels, however, so you may need to get that master’s degree that you’ve been putting off! Don’t worry if you weren’t a computer science major during university. There are a lot of people from various backgrounds working on AI. I’ve worked with astrophysicists, doctors, and even mechanical engineers. That’s the beauty of our field.

Make sure you’re committed, and don’t give in to the hype. There are people in the industry who still don’t understand how it works and keep placing high expectations. Don’t go in it thinking that you’re going to recreate the movie I, Robot! AI is only capable of doing very specific tasks at the moment, so keep your expectations grounded. But who knows? Maybe in five years, AI will be completely autonomous and will try to subjugate humans thanks to one of your discoveries!

If you want to learn more about Artificial Intelligence, check out these courses:

Artifical Intelligence Boot Camp | 1-Day Classroom or Live Online | Private Onsite

Artificial Intelligence with Deep Learning Workshop | 3 Day Classroom or Live Online | Private Onsite