What is an AI Engineer?
Business + ManagementFeatured PostAccelerate AICareerOpinionsposted by Elizabeth Wallace, ODSC June 3, 2019 Elizabeth Wallace, ODSC
AI is coming for your business, and at the most recent ODSC East keynote, Michael Stonebraker of MIT and Tamr laid it all out for us. If you aren’t ready to hire the experts, you won’t be ready.
AI Engineers are going to be the next biggest thing because they’ll provide the expertise you need to build your models. Let’s talk about what AI engineers do and how you can utilize their expertise to bring your business into the next generation.
[Related article: What Will the Next Generation of Data Scientists Look Like?]
What Are AI Engineers?
AI is a complex network of algorithms that think like a human brain. Programming and training AI takes time and expertise but not only for the work of deploying the models. You must also screen for problems, handle system maintenance, and make improvements. Yes, you can find people to write algorithms, but what you want is someone who can make decisions about the system itself.
AI engineers build and test AI models. They must be able to move between traditional software development and the unique needs of AI learning. They also navigate the learning spaces of their neural networks and the business value those networks provide.
Once your organization moves to AI-driven initiatives, it will need someone to be the point person for creating and evaluating those algorithms. Consequently, the position is a hybrid of data engineering, software development, and data science. So, what’s the big difference from what you have on your team now? AI Engineers understand how to deploy machine learning.
AI engineers must:
- be familiar with a variety of systems including cloud-native systems and chips.
- understand the principles of deep learning
- decide when models are ready to deploy and maintain them for accuracy (plus unintended consequences).
How Do I Become an AI Engineer?
AI engineers need a deep understanding of the programming involved in deep learning. This isn’t traditional programming. These algorithms perform without human supervision, and altering learning involves making changes to the neural networks rather than to individual strings of code.
There’s great news, however. You don’t have to come exclusively from a software background to land an AI engineer position. The field is very new, so AI engineers come from a variety of backgrounds, including physics or biology. If you’re already in biology, for example, learning the software skills to build for AI within healthcare or drug discovery could be an excellent career path.
Without a technology background, you’ll need training for full stack engineering. And if you already have that down, you might want to focus on a particular field. Niching down to a specific area could help you build better AI models because your knowledge is targeted.
What Are Promising Fields for AI Engineers?
We’re predicting that AI Engineers will be just as familiar as software developers are, but for now, a few fields are most promising. Let’s take a look at where you might find this new type of position.
- Enterprises: Businesses with large amounts of unstructured data are already making use of AI to pull key insights from unstructured data. Think social media posts and visual media. Building enterprise solutions in the form of deep learning could help launch a business into a new age.
- Healthcare: Healthcare is full of processes that take a massive amount of time and resources. The insertion of AI into time-intensive healthcare tasks such as drug discovery or analysis of echo-cardiogram output could reduce cost and wait time. Building those models could help revolutionize our healthcare system.
- Manufacturing: Manufacturing could see the most significant changes thanks to AI. IoT integration, predictive maintenance, and revamping the supply chain with AI’s deep learning models could help make our products safer and cheaper. We can even improve our search for safer and more affordable materials through AI experimentation.
- Finance: Fraud detection is one area where deep learning shines. Instead of applying fraud markers across the board, machines can learn the habits of individual customers and flag for fraud. They’ll be able to reduce the instances of false positives and grow with customers’ life changes. Plus, blockchain capabilities, predictive analysis, and IoT could soon make finance unrecognizable from where it is now.
[Related article: Data Scientists Versus Statisticians]
Build a Career in AI
AI engineers will be experts who can decipher and deploy the deep learning concepts we’ll need to make sense of the data we produce. As businesses and fields come to grips with the sheer amount of data, they’ll be hiring experts. We’re moving into an age where machines help us process information and reduce our workloads. AI Engineers will provide insights to organizations required to stay competitive, explore answers to persistent questions, and build solutions where there were none before.