Artificial Intelligence (AI) Engineer is a job title of the future more than it is of the present. At this time there are, relatively speaking, only a few professionals in the tech industry whose title is AI Engineer. Many of them are outstanding software engineers with illustrious backgrounds, working in senior positions for tech giants like OpenAI, Meta or Tesla.
Yet all of this is about to change. As AI marches onwards in its unstoppable expansion, AI engineers will soon become a staple not just of all tech companies, but of all sorts of everyday businesses. In other words, the job is about to be normalised.
This means that now is an excellent time to ride this wave of change and start picking up some AI skills. But what exactly does that entail? What is an AI engineer, and what sort of work do they do?
We’re here to answer exactly those questions!
- What is an AI engineer?
- What sort of skills does an AI engineer need?
- What does an AI engineer do?
- Does an AI engineer do coding?
- Does an AI engineer need maths?
- Is AI engineering hard?
- Is AI engineer a good career?
What is an AI engineer?
An AI engineer develops the software architecture required to train algorithms to become ‘intelligent’, and then deploys them to solve specific problems. They must identify the areas where a company’s operations can make optimal use of artificial intelligence solutions, meaning they also need a strategic business outlook that ordinary programmers don’t have.
An AI engineer is usually a very advanced type of machine learning engineer or data scientist. Some may have a different academic background (e.g. Computer Science, Mathematics or Physics), but everyone has to engage with the world of data and machine learning at some point.
This is because the foundation of what is meant today by ‘Artificial Intelligence’ lies in machine learning models, like artificial neural networks, supervised and unsupervised learning, and K-nearest neighbours algorithms. These are all topics that a data scientist will be familiar with.
There exist other kinds of artificial intelligence, from simple mechanistic models used in videogames, to theoretical concepts like ‘general artificial intelligence’, which does not yet exist. But these are purely semantic distinctions, at least at this moment in time. In the modern tech scene, an AI engineer is essentially a machine learning engineer with a particular specialisation.
What sort of skills does an AI engineer need?
An AI engineer needs strong programming skills, an all-round command of machine learning tools, a reasonable foundation in statistics and probability, and a general understanding of business strategy. They need to be independent, analytical and communicative.
If we were to compile a list of the ideal skills an AI engineer should have, it would look like this:
- Advanced coding skills with one or more programming languages suitable to processing data. That means Python, R, or SAS. Knowledge of SQL is also strongly recommended, although that is a much simpler language.
- A deep understanding of machine learning concepts like neural networks, random forests, supervised learning, reinforcement learning, inclusive of an ability to distinguish which model is most useful for which job.
- Knowledge of statistics and probability, not necessarily to a Masters or PhD level, but enough to comfortably work with its concepts, and with numbers in general. The actual calculations will always be performed by the machines, but the engineer must know what’s happening beneath the hood.
- The business understanding required to know which uses of AI would be most helpful for any given company, given the products they sell and the market they operate in.
- The communication skills needed to explain to the rest of your company how everything works – you won’t have many people around you who understand what you’re doing!
- A solid grasp of AI ethics. If this seems like a ‘philosophical’ concern, you’d be mistaken – unethical implementation of AI not only hurts other people, it also opens legal liabilities and could potentially bankrupt a company.
What does an AI engineer do?
An AI engineer builds and deploys the software systems that a company will use to automate its business processes. This may include internal processes, like collecting and drawing insights from data about its product, or external processes like interacting with customers through chatbots, or guiding them through recommender systems.
The work of an AI engineer can vary considerably depending on the company, but generally it will include one or more of the following three phases.
Firstly, an AI engineer develops strategy. This means they study their company’s products and operations, and align with management to identify how AI can help them simplify, optimise and automatise what they do.
Secondly, an AI engineer builds the AI system itself. This is the meat of their work, and not something on which they’ll work alone. It involves building a machine learning model and training it with the company’s data so that it learns how to respond to different problems and situations. These models must then be integrated into application programming interfaces (APIs) that allow the rest of the company to interact with the AI system as a whole.
Thirdly, an AI engineer must maintain the system that they built. This means refining its performance as time goes on, troubleshooting when necessary, and expanding the uses and features of the system as appropriate.
Does an AI engineer do coding?
An AI engineer has more responsibilities than an average programmer, but yes, they absolutely will be expected to write and understand code. This is not a job that can be done without learning to code.
Does an AI engineer need maths?
An AI engineer will need a reasonable understanding of statistics and probability, and they can’t be scared of working with numbers. However, pure mathematics is not really the bread and butter of AI engineering. Contrary to what many people think, it is not necessary to be a brilliant and highly advanced mathematician to work as an AI engineer.
Is AI engineering hard?
AI engineering is very hard right now, but will get easier in the future. At the moment there simply hasn’t been enough time for the industry to absorb the AI revolution, meaning that AI engineers must navigate uncharted waters, and have very limited resources when they need to solve a problem.
However, as AI becomes more and more widespread in business, the role of AI engineer is likely to become standardised. Best practices will be established, StackOverflow will fill itself with solutions to AI problems, communities of AI engineers will form. More and more university courses as well as Data Science bootcamps are integrating AI into their curricula, meaning future professionals won’t have to learn by themselves.
This means that AI engineers will likely see their salaries go down a little, but it also means that it will be much easier to learn the discipline and access the industry.
Is AI engineer a good career?
Studying to become an AI engineer is an excellent investment in your long-term future. The industry is booming, and demand for these professionals is climbing just as quickly. However, it is not a job you should expect to land in the short term.
Instead, you will need to learn about data and machine learning, then work your way through at least a few jobs that put those skills to use, and in the meantime keep up to date with AI developments and applications.
AI engineering is a very high-tier job in tech at the moment, but in time it will settle down into a new normality. If you want to work with AI, then seize the opportunity and start learning now. You’re still ahead of the curve.