Skills Needed in AI to Build a Future-Ready Workforce

Sue Razick, Actalent Strategic Operations Recruiter – Software/Hardware/Systems Engineering

Learn how to recruit and hire engineering and science candidates with the skills needed in AI to build a future-ready workforce.

Adjusting to AI in Engineering and Sciences

The usage of AI in the workforce continues to disrupt engineering and science spaces, including transportation, automotive, and aerospace and defense, by enhancing efficiencies and optimizing operations. But while it’s changing roles and creating opportunities for workers and decision-makers alike, the pace of evolution and the AI knowledge gap among hiring managers can make building a future-ready team challenging.

To keep up with an ever-evolving workplace, it’s essential to future-proof your team and ensure you’re hiring for the in-demand AI skills you need for today and tomorrow. To do this, consider how you can work with technical leads, staffing partners and contractors to find the right contract talent for every stage of the project.

Create a Tech-Informed, Realistic Job Description and Identify Your AI Skills List

The challenge begins with understanding exactly where efficiencies need to be made within your company. This will impact the processes you’ll need to update, and what skills will be necessary to achieve those goals.

Once these are determined, you’ll have a better understanding of the workforce you’ll need to hire. You can begin working with your technical leads to develop a job description that accurately reflects the responsibilities of the role(s).

When preparing to build a future-ready workforce, many hiring decision-makers look specifically for AI experience. But it’s important to remember that AI isn’t one specific skill set — it comprises knowledge of and experience with many different programs. This leads to many companies looking for a “unicorn”: one candidate who has the technical experience and expertise to fulfill what should be multiple roles.

However, holding out for a unicorn could lead to a long, unproductive and expensive search. Instead, work with your technical leads to ensure your job description is reasonable and consistent with the market. This may mean creating and hiring for a few new positions, rather than just one.

At this stage, you should also work with your tech leads to distinguish between the skills they would consider “must-haves” versus “nice-to-haves” when reviewing candidates. Knowing what skills and experiences to prioritize will help you find a capable hire more quickly.

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Learn to Conduct Deeper-Level Interviews

During recruitment, don’t rely solely on resumes to get the full picture of a candidate’s experience. Instead, use the interview process as a chance to dive deep into their knowledge and ability to grow and adapt to new technology.

Look Beyond Resumes to Find Needed AI Experience

Because “AI skills” encompass many different areas of knowledge, you should call out the specific programs or products your new hire will be utilizing. For example, this could include familiarity with cloud computing services like Amazon Web Services (AWS) or Google Cloud Platform (GCP), or Rust or Golang development.

It’s important to remember, though, that the market for talent with AI skills is still growing, and many experienced candidates do not think to highlight these specific skills and programs on their resumes or in interviews — even if they have the expertise. So, don’t discount those who have transferable skills or experience but neglect to include certain buzzwords or products.

Instead, look to interview candidates with relevant work experience. The interview will give you the opportunity to engage candidates in conversation and inquire about these specific AI-related skill sets. This will yield a more nuanced view of their knowledge and experience with relevant AI skills than a line in their resume.

Differentiate Between Candidates with Identical Skills

Once you progress to the interview stage, you’ll likely find multiple candidates with the same AI skills and experience. At this point, your goal will be to differentiate between very similar candidates to find the right fit.

One way to do this is to look for the candidate who can best explain complex technical processes in a clear way. This shows a greater depth of understanding and command over complex concepts. It also helps ensure the candidate can communicate and collaborate successfully on a team of workers with diverse knowledge and roles.

Another factor to consider when distinguishing between candidates is to choose the person with the greatest willingness to learn and an intentional curiosity about the field. This means looking for candidates who either have a demonstrated history of continual learning and upskilling or show an interest in how AI tools can support them in their role. As technology continues to evolve and the AI skills gap deepens, these candidates will be best equipped to take advantage of new advances and incorporate them into their roles for improved performance and efficiency.

Find Support from Contract Talent

It can be difficult to truly build an all-encompassing, future-ready workforce. As projects evolve or end, or new software or products are introduced, the skills your team needs will likely change.

To keep pace with your needs, consider bringing in contract talent to support each phase of the project rather than hiring for full-time roles. This will allow you to hire for each of the niche skills you need exactly when you need them. This strategy eliminates the need to find a unicorn candidate or hire multiple full-time roles, saving your business time and money.

Streamline Your AI Talent Recruitment with Actalent

Working with a partner like Actalent can help you streamline the recruiting, hiring and onboarding process while bringing in additional industry expertise.

Our recruiters work with your internal team to identify the skills needed, taking into account the products and technical perspectives that will be utilized throughout the project. This goes beyond just knowing the job description. Instead, we work to understand the day-to-day responsibilities of the position. We’ll also meet with your hiring panel to understand the most important technical skills a candidate will need, while having conversations around what what’s essential and what “nice-to-have” skills may be accessed via training and upskilling.

From there, we can access our pool of highly specialized, nationwide talent to find candidates who meet your needs. Our recruiters have the technical and industry expertise to identify talent who possess even the most niche AI skills. And, thanks to our rigorous screening process, you don’t need to take the time to recruit and interview candidates who aren’t a good fit.

Frequently Asked Questions

What are artificial intelligence skills?

Artificial intelligence skills include both an understanding of the building blocks of AI technology, including machine learning, neural networks and data analytics, as well as experience with key AI tools.

What are the skills required for an AI engineer?

It’s unlikely you’ll be hiring for a position as general as “AI Engineer” because of the diversity in AI experience and skills. Instead, you’ll likely be looking for an engineer that has experience with the AI tools and programs most relevant to your specific projects.

This means that, in addition to the appropriate engineering degree, you’ll want to seek a candidate with knowledge of, or experience in, AI-related skills like machine learning, natural language processing, cloud computing and data analytics.

What AI skills are in demand?

The AI skills you need will be largely determined by the specific projects and programs your company invests in. However, some common AI programs and skills used in engineering and science spaces include familiarity with cloud computing services like AWS or GCP, or Rust or Golang development.

How is AI used in the workforce?

There are many uses for AI in engineering and sciences. However, one of the biggest contributions AI tools can make is in helping automate repetitive work. This allows for greater optimization and efficiencies, leaving workers with the bandwidth to focus on higher-level projects. Some examples of AI automation in engineering include data processing, predictive maintenance and autonomous systems.