What career paths are available with AI and how do I get started with them?
When considering a long-term career in AI, there are essentially four main career paths — AI Tool User, AI Developer, AI Engineer, and AI Researcher.
Each of these career paths entails a different level of engagement with AI.
As a result, each path requires a different set of knowledge, skills, and education.
Metaphorically, we can think of these four levels of knowledge and engagement using an automobile analogy.
There are people that know how to drive a car, people that can customize and repair a car, people that can engineer a new car from scratch, and people developing the future of vehicular transportation.
In this article, I’ll be using this automobile analogy to help you better understand the four main AI career paths available today.
An AI Tool User is someone who uses AI-powered tools to complete day-to-day tasks but doesn’t really understand how the AI powering their tools works. These tasks are often as simple as talking to a digital voice assistant, using drag-and-drop data visualization tools, or writing an English query in a BI tool.
Using the automobile analogy, an AI Tool User is someone who knows how to drive a car. A person can learn how to drive a car with just a bit of basic training. Then, once they know the fundamentals, they can drive almost any automobile and effectively navigate the various streets and highways in our world.
As a result, an AI tool user doesn’t really need much training in AI to be effective in their career path. They just need basic AI literacy, an understanding of the capabilities and limitations of their AI tools, and they need to know how to use the specific types of AI-powered tools that they are working with.
If you’re interested in this career path, you don’t need to go back to school to acquire these skills. Rather, you can learn these skills through online courses and daily practice. This is by far the easiest of the four career paths to choose from and it will likely be a minimum requirement for most jobs in the future.
An AI Developer is someone who adds AI capabilities to software applications and systems. They use pre-existing AI services or pre-trained ML models to implement these features into their applications. This is often as simple as making a REST API call to a web service hosting an AI/ML model.
Using the automobile analogy, an AI Developer is someone who can customize existing cars, perform an engine tune-up, and repair a broken transmission. They understand the main components of the vehicle, how they all work together, how to assemble them, and how to repair them when they break.
As a result, an AI Developer needs to have knowledge of the various AI techniques and pre-trained ML models. They need to know how to use transfer learning to customize existing models. And they need to understand the capabilities and limitations of various AI/ML models and techniques — plus AI ethics.
If you’re interested in this career path you don’t need to go back to college to learn these skills either. If you already have programming skills, then you should be able to augment your skillset through online courses and with lots of practice. These cloud-based AI services are rapidly becoming pretty easy to use.
An AI Engineer is someone who creates new AI systems from scratch. They implement AI algorithms and train custom ML models using data collected from various sources. They program AI decision-making systems, train ML models to predict outcomes, and build robots to perform manual labor.
Using the automobile analogy, an AI Engineer is the equivalent of an automotive engineer. They can design a new automobile from scratch, ensure that it functions properly, and get it into production at scale. They understand every component of a vehicle and all of the math, science, and physics involved.
As a result, an AI Engineer needs to understand a wide variety of AI techniques and ML training algorithms. They need to work effectively with data, implement AI techniques, and train ML models from scratch. They also need to understand the various pros/cons of each algorithm and technique.
If you’re interested in this career path, I recommend at least a four-year degree in computer engineering, software engineering, computer science, data science, or an equivalent field. You need to understand the math and statistics involved in the models that you are training to evaluate their effectiveness — online courses alone won’t be enough.
An AI Researcher is someone who develops entirely new AI techniques and creates new ML training algorithms from scratch. This is by far the most complex and difficult of all of the career paths so it is only reserved for the best of the best. You need to really know your stuff to fulfill this role.
Using the automobile analogy, an AI Researcher is equivalent to someone developing entirely new modes of transportation or pushing the limits of automotive design with cutting-edge technology. They have in-depth knowledge of the math, physics, and engineering of automobiles.
As a result, an AI Researcher needs to understand the foundations of AI and ML. They need to understand all of the math, statistics, and science involved in researching new AI/ML algorithms. Not only do they need to know the state-of-the-art in AI/ML but also what may be possible in the future.
If you’re interested in this career path, I recommend that you pursue either a Master’s or a Ph.D. in computer science, data science, artificial intelligence, or an equivalent field. You really need to understand the math, statistics, and research methods involved to push the state-of-the-art forward.
Knowing which of these four career paths are of most interest to you will help you to understand what education and skills you will need to be successful in your role. If you would like to know more about a future career in AI, please watch my free online Preparing Your Career for AI.