November 20, 2023
min read

How to Become an AI Engineer

Luis Minvielle
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By 2030, AI could contribute up to $15.7 trillion to the global economy — more than China and India's combined output in 2023. Overall, AI is still at a very early stage of development, and companies and governments need people who know how to harness its power. It's no surprise that AI engineers are in such high demand today. 

AI engineering is a specialised field with promising job growth, and has already increased by 23% from 2022 to 2032. Where do you start if you want to get into such a specific field? We’ll discuss how to get into AI engineering, its potential salary, and the future outlooks this job would bring. 

What is an AI engineer?

An AI engineer develops and improves artificial intelligence systems using programming and data analysis skills. Their specialisation is often machine learning and neural networks. AI engineers will be in the spotlight in the upcoming years since many organisations are pushing for responsible and ethical use of AI technology.

It’s not the only role in AI, though. Some developers might instead focus on deploying LLMs, keeping the infrastructure stable, or even creating synthetic data to train the models — more of a data scientist role. An AI engineer is also different from a prompt engineer.

How to get into AI to become an AI engineer

According to programmers, to become an AI engineer, you should “first become an engineer. Then, learn AI and ML. It’s easier said than done. Becoming an AI engineer is a complicated process. Here are some general steps on how you can start getting into AI:

1. Fundamental first: Gain a strong foundation in mathematics and statistics

Understanding key concepts such as linear algebra, data structures, algorithms, calculus, and probability theory will give you a significant advantage when learning AI and machine learning principles. Have you considered enrolling in a maths course? AI heavily relies on maths, especially statistics, linear algebra, and multivariable calculus. If you’re looking at graduate schools to pursue further studies, know that a weakness in those areas has a good chance of getting your application immediately thrown into the dustbin.

Many developers must focus on this first step and jump right to the second. This will cost you in the future, as most jobs will look closely at your formation rather than projects you’ve done. Learning programming languages will also be much more accessible if these basics are covered through and through.

Being highly proficient in maths separates a mere coder from an expert. It may suck not to binge-watch The Weeknd’s panned TV show now, but you’ll thank yourself in 5 years. 

2. Learn programming languages

What a beginner would think is the most crucial part of the process, but — which ones to learn? Where to start? Focus mainly on Python and R as these are the most commonly used languages in AI and Machine Learning. Many machine learning libraries are in Python — TensorFlow, Keras, and PyTorch. Stack Overflow’s programming language trends clearly show that it's the only language on the rise for the past five years, and it’ll be helpful for life. If you don’t know how to start learning and prefer a hands-on approach, check the best open-source projects to contribute and pick Machine Learning-related tasks. 

Source: Stack Overflow Insights

3. Study algorithms and machine learning concepts

This will be necessary when you’re in an interview and can’t check forums for answers. You better start learning about different types of machine learning (supervised, unsupervised, and reinforcement learning) and understanding standard algorithms such as linear regression, decision trees, support vector machines, and neural networks.

4. Add degrees and courses into the mix

Try out as many AI fields on your own as you can. Pick the few that you’re really enthusiastic about and push as far as you can in academia on them. These programs will provide structured learning and access to expert guidance. It's true that the higher up you go, the fewer jobs will be available. However, it's also true that the employment ratio to candidates available will increase as you go higher up. You might get a job without even applying, though. OpenAI, for example, built its roster by selecting highly regarded machine learning experts from academia.

If you want to pursue a Ph.D., look at professors you would like to work with. Do some undergrad research related to their work. If you're going to do a master's, shoot for a school with a robust program and many AI–ML professors.

If you pursue a Ph.D., more recruiters will pay attention to your resume, but if you want to start working faster, a research-oriented master's will get you working. AI engineering is ultimately an applied field, so working IRL will teach you a great deal.

5. Gain practical experience

One of the most critical steps is applying your knowledge to real-life projects. Don’t rush this one. Give yourself time to assimilate the concepts you have learned by working on small, gradual projects. You can start with these as you study. As you nail the simpler ones, escalate to more complex ones. Use datasets available online or participate in competitions to refine your skills. 

byu/fiveMop from discussion

6. Build a portfolio 

Here’s where you’ll showcase all your projects and accomplishments. Include code repositories, project descriptions, and other relevant data demonstrating your skills and expertise. Making a portfolio website where you create models and write about them will make you stand out as a candidate.

7. Stay updated 

Participate in online communities, attend conferences, and subscribe to newsletters for the latest information. What will get you far is the will to stay on top of the latest methodologies and the experience to tell people that a logistic regression or a hiked-up decision tree is better than deep learning from their problem. 

What AI-related jobs can you apply for early on? 

You don’t have to wait years before getting an AI-related job. In the end, it’s a growing industry, and even if it will allegedly wipe out every white-collar job on Earth, it will probably have a few open positions in the meantime. So, check out Applied Machine Learning (AML) or Data Science teams instead of more traditional pure ML research teams. You must go for the research engineer or scientist positions to be directly involved in AI. It's a great place to learn about ML–AI without being expected to know enough to produce research, and it's an accessible role for anyone with a bit of experience as a software engineer and an interest in ML–AI.

Salary outlook for AI engineering 

According to Glassdoor, the average salary for an AI engineer in the United States is $132,228 annually. With a projected job growth of almost a quarter between 2022 and 2023 and a top 3 ranking on LinkedIn’s list of jobs with the most instantaneous growing demand in 2023, a bright future awaits AI engineers.

ML–AI skills will remain an important asset, and those who know how to apply them in a business to make money will stand out. 

The key here is focusing on building currently impactful AI products. What moves the needle for investors is profits, or at least a promise of earnings down the line.

Is it just a phase? Will AI make software engineers obsolete? 

AI is here to stay. Companies focus on profits, and since AI has no salaries, it seems to have the edge against devs when defining the workforce for the coming times. Still, developers have nothing to worry about. IDEs and cloud computing have increased developer productivity for decades. Arguing that AI will eliminate jobs is akin to claiming that Docker will eliminate jobs. It will, but it will also help bring new ones to the job boards.

Software development is the act of solving problems, and you’ll still need people to define and solve them. These will then use AI to increase productivity, which explains the astonishing forecast regarding the increase in the global economy. There is a long way before technology is good enough, and regulators are confident that we’ll be able to trust and implement it for the general population, but the time will come. It's just a matter of leveraging slow human decision-making and regulation changes in an advantageous way. 

So, no, AI is not just a phase. Plenty of people forget that AI has been around for a long time. Some AI features have become so mundane — e.g., when you turn on your GPS, and it calculates the best route — that they’re no longer acknowledged as AI.

Now, thinking that AI will make software engineers obsolete is an indisputable mistake. It's like judging a power drill would make a junior electrician like a senior who’s been building houses for two decades. Yes, the power drill will speed up the junior on the job, but not better — if incompetent, then handling a power drill can enable them to make things worse — but this won’t transform a junior into a senior.

Landing your first AI engineering job 

Most people working on serious ML roles without a Ph.D. arrive after spending time in AML–Data teams. Don’t know where to find these? Browse through our job boards. You’ll see that at WeAreDevelopers we’re constantly vetting and featuring plenty of open job positions that will be perfect to start. 

In any case, a long way awaits you before you land your first AI Engineering job. If you check out our site today or after your graduation party in years’ time, you’ll spot that at WeAreDevelopers, we are regularly reviewing and highlighting the greatest positions in Europe. Maybe when we meet again in some years' time, an AI tool will be the one speaking to you. But an AI engineer will be behind it, so don’t worry. Cheers for a successful AI career.

How to Become an AI Engineer

November 20, 2023
min read

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