I’d bet you’ve had some encounter with artificial intelligence in the past week. If you’ve applied for a job, your application likely made its way through an AI hiring tool or ATS. If you’ve messaged a customer service hotline, had Gmail help you craft an email, or scrolled through social media, you’ve also used AI (or rather a subset of AI known as machine learning).
Or maybe you’re one of the millions of people leveraging ChatGPT, Microsoft’s Copilot, or Google’s Gemini to nail your job interview or work project, write a novel or song, or plan a trip abroad. These chatbots and other related tech fall under an emerging category called generative AI, or GenAI—and it’s booming in popularity and demand.
Investors want to fund generative AI growth, and companies want to use and build generative AI tools—and neither can hire enough talent to get the job done.
Last year, Bloomberg Intelligence reported the market for GenAI could grow to $1.3 trillion in the next 10 years, up from just $40 billion in 2022. Labor market analytics firm Lightcast saw demand for generative AI skills in job descriptions grow 1,848% in 2023 in the wake of the AI tech boom.
“We expected even getting into the beginning of this year, things would perhaps slow down to some degree, but to our surprise, and in some cases our delight, things have really been the opposite,” says Aaron Sines, the director of talent, AI and machine learning, at technical recruiting company Razoroo.
In particular, he’s noticed that interest in generative AI has shifted outside the tech sector, with companies in finance, healthcare, manufacturing, and government wanting a slice of the AI pie. “Nobody wants to get left behind.”
As a job seeker or professional, you too might be wondering how to stay ahead of the curve. Which is why we asked Sines the top generative AI skills he’s seeing employers seek right now, and why they’re so important to develop—even if you don’t want to work in AI.
What is generative AI?
In simple terms, Sines says, generative AI is “all about generating and creating content,” be it images, text, or audio. Generative AI models are fed massive sets of data, which they use to generate responses to “prompts”, creating a human-like interaction between user and machine.
To learn more about generative AI and its applications, take a look at OpenAI, Google, Amazon, and other companies focused on this work.
Won’t AI just take my job? The benefits of building generative AI skills
You may have gotten this far and thought, “Oh great, all this investment and advancement in AI means I’m gonna be out of a job soon anyways. Why should I even bother learning about it?”
Sines admits that some companies are turning their budgets away from hiring talent and toward technology as an alternative solution. But many others want humans and machines to coexist in the workplace in the hopes of offloading tedious tasks, improving productivity, and leaving room for new innovations.
This is where workers, even individuals outside traditional tech jobs, can benefit from expertise in AI. To put it another way, Sines says: “You’re not going to be replaced by AI, but you’ll get replaced by a person who could use AI.”
Beyond making you a competitive hire, AI skills can make you better at your job by supplementing and assisting with tasks that might be challenging, boring, or time consuming. “It can help us be more creative, especially with our problem solving,” he adds.
The 10 most in-demand generative AI skills right now
Sines has noticed that demand for generative AI skills has pivoted away from general knowledge and toward real-life applications.
“Before, we saw a lot of AI research roles where they’re trying to think of theoretical use cases, but, this year, the stark contrast is it’s all about ROI. It’s all about practical use cases of AI,” he says. In other words, as we’ve gotten better at understanding this new technology, we’re now searching for ways to implement it into our businesses.
Most of the generative AI skills needed by today’s employers are specifically aimed at engineers, IT specialists, data scientists, and technologists looking to upskill, specialize, and stand out from the pack.
“On the technical side, they are looking for talent that can build these systems, and on the non-technical side, people who can work alongside these systems to provide faster response times and accuracy in troubleshooting tickets,” says Sines.
That said, many of the AI skills listed below can be helpful for professionals in a variety of fields, from medicine to marketing.
1. Deep learning
Deep learning is a subset of machine learning that uses multiple layers (hence “deep”) of neural networks—an artificially created network modeled after the neurons in a human brain—to make complex decisions. The idea is to teach machines to mimic human behavior.
Sines has seen this skill set come in handy in manufacturing, healthcare, and hospitality in particular. “There’s been a lot of buzz around robotics and physical applications of robotics finally having their moment because of AI,” he says.
2. Computational biology
Computational biology focuses on leveraging data (sometimes with the assistance of GenAI models) to better understand biological systems and relationships. In medicine and healthcare, this skill can be applied to drug development or genetics research.
3. Hardware optimization and architecture
Experts in hardware optimization and architecture can maximize the relationship between AI software and hardware so everything works seamlessly and effectively.
It’s “all about bringing AI solutions into real hardware and hardware applications. This could be through camera systems, imaging, through print systems..manufacturing, medical—these are some big, big areas that we’re seeing a lot of growth in,” Sines says.
4. Prompt engineering
Prompt engineers, or specialists in prompt engineering, design inputs that ensure what a generative AI model delivers is optimal for whatever circumstance it’s targeting. As generative AI models are built and iterated upon, prompt engineers help train them.
But being an expert in prompt engineering can be useful for other contexts. If you ever use ChatGPT, knowing exactly what prompts to feed it will ensure you get accurate results faster and with minimal hand holding.
5. Natural language processing
Natural language processing (NLP) allows computers to comprehend and manipulate human language. It’s the fuel behind many GenAI models, and a skill also applicable to tasks such as gathering customer sentiment, translating documents from another language, or even spotting phishing scams in your inbox.
6. Computer vision
Computer vision skills help you train machines to read and identify imagery. One common use case is the feature on Facebook or your photos app that tags friends automatically—but the skill has incredible potential in transportation (think: self-driving cars) and patient care.
“These tools, and these individuals I should say, are going to use these tools to work on projects like improving medical imaging such as MRIs, CT scans, really just enhancing overall image processing solutions,” Sines says.
7. Model optimization
When vetting candidates for roles in AI, Sines always looks for model optimization experience with tools such as TensorFlow. This skill matters more now than ever as companies building or implementing generative AI seek to make their models as efficient as possible.
8. AI ethics
AI ethics involves incorporating principles and best practices that ensure any AI model or tool is benefiting society as a whole. Someone with this skill set anticipates and plans for biases that might arise during the training of these models. This aims to prevent potential harm to the business, as well as to individuals or groups.
“Our clients want to see that candidates have familiarity with ethical considerations and just broader implications when it comes to GenAI,” says Sines.
9. Data management
Data management helps companies properly handle the often overwhelming amounts of information coming in. Sines says he frequently has clients looking for someone who understands storage architectures, has experience managing large data sets, and knows how to optimize data collection and analysis.
10. Continuous learning
The biggest soft skill Sines says he looks for when hiring for roles in AI, GenAI, or related fields is an eagerness to learn. As the technology advances rapidly, he notes, companies want “someone who’s willing to adapt and evolve to the different tools and methodologies that are out there.”
Maybe you already have some or all of these skills. Fantastic! But how can you apply them to your job search to land that dream role?
Sines advises collecting stories and interview answers that show how you have (or would) use AI in the position. For example, he says, “a healthcare organization likes to see someone had exposure to AI solutions deployed in healthcare or at least some understanding of how things can be used in healthcare.” Another strategy is to showcase how you’d solve specific problems or improve a process using generative AI.
Also, he suggests building a network within the AI world—advice he follows himself to match the expertise of the candidates he interviews. This could mean following popular GenAI influencers or engineers, subscribing to tech research or newsletters, or attending conferences. “Just staying really embedded in the community is probably one of the more important things,” he says.