10 Skills To Look For When Making Your Next AI Hire (Hint: It Is Not Just About Coding Anymore)

When most people hear “AI job,” they imagine someone buried in Python scripts, juggling TensorFlow models while whispering sweet nothings to an AWS instance. And sure—those technical chops still matter. But the real differentiators in today’s AI talent pool? They go far beyond code.

Let’s Be Real—The Basics Still Matter

Let’s talk about fundamentals before we get carried away with all the next-gen skills. You still need people who know how to build the engine before they start dreaming about the sports car.

1. Python: It is still the go-to language for anyone doing serious AI work. Simple, powerful and packed with libraries that make life easier.

2. Machine Learning: The buzzword might be overused, but the skill itself? Still critical. Teams need folks who understand how models work, not just how to plug in frameworks.

3. Data Science: This is ultimately about making sense of messy, real-world data. If someone cannot do that, the rest does not really matter.

4. SQL: It is not flashy, but it is still essential. If your team cannot pull and manipulate data, they will be stuck before they even start.

5. Cloud Platforms (AWS, Azure, GCP): AI is not something you run on a laptop anymore. Knowing how to get models up and running in the cloud is just part of the job.

These are not “nice to haves”; they are the minimum bar. But here is the catch: Checking these boxes will not make someone stand out anymore. It is what they bring on top of this that moves the needle. But here is the twist: These technical skills are just the price of entry. They get you into the room, but they do not win the room. That is where the next set of skills comes in.

Beyond The Code: The Next Generation Of AI Talent

Let us take a page from the real world. The most successful AI professionals I have worked with—across startups, Fortune 500s and our own AI consulting practice at Sonatafy—share one thing: They understand that AI is not just about building models. It is about building impact.

Here are the other five skills separating the good from the game-changers:

6. Problem-Solving Mindset: You can train a model, but can you frame the right problem? AI is not magic; it needs to be pointed at something meaningful. Talent that can dissect and translate a business challenge into a solvable AI problem is invaluable—it is less about code and more about context.

7. Creativity: Creativity has become a competitive advantage in a world filled with templates, frameworks and plug-and-play solutions. Creative AI engineers ask, “What if?” They repurpose tools in unconventional ways, explore new datasets and are afraid to fail fast (and smart).

8. Business Acumen: Here is a hot take: Your next AI hire should be able to read a P&L. Why? Business fluency empowers technical teams to align their work with ROI, customer value and long-term strategy. AI for AI’s sake is dead. Long live AI that delivers outcomes.

9. Communication Skills: It is one thing to build a great model. It is another way to explain it to stakeholders, navigate ethical concerns and collaborate cross-functionally. AI leaders must present complex ideas, especially in boardrooms with non-technical decision makers.

10. Ethics And Responsibility: With AI’s growing power comes growing risk. Bias, fairness and explainability are not just academic topics—they are operational concerns. Today’s top talent must build responsibly, understanding both their work’s technical and societal implications.

Closing The AI Skills Gap

Here is the reality check for hiring managers: The AI skills gap is not just about not having enough people who know TensorFlow—it is about lacking talent who can connect the dots between code, customer, compliance and company strategy.

At Sonatafy, we often see companies trip up here. They hire for “model accuracy” and have teams that cannot ship usable products. Or worse, they spend millions on an AI initiative that never aligns with the core business because no one thought of asking, “How are we doing this in the first place?” The future of AI hiring is not about deeper specialization; rather, it is about strategic integration. In other words, you are not just hiring a developer—you are hiring a business builder.

What This Means For Tech Leaders

If you are a CTO, CIO or engineering leader, here are your marching orders:

• Rethink your hiring scorecard. Are you prioritizing well-rounded candidates with tech and business perspectives?

• Invest in upskilling. Pair your engineers with product managers. Send your data scientists to design sprints and host AI ethics workshops. Create space for cross-pollination.

• Look nearshore and beyond. The best talent may not be across the hall. At Sonatafy, we have found incredible success tapping into LATAM hybrid onshore-nearshore teams who bring both cost advantages and cross-cultural agility and business fluency.

• Push for diversity of thought. Do not build a team of Python clones. Build a team that will challenge assumptions, be willing to ask questions and think like entrepreneurs.

The Bottom Line

AI is no longer the playground of coders—it is the sandbox of strategic thinkers.

The companies winning the AI race are not just finding great technical talent but are building multidisciplinary teams that speak business, think creatively and execute responsibly.

So yes, keep hiring the Python rock stars. But make sure they know how to connect that model to your mission. At the end of the day, AI is not just about writing better code. It is about writing a better future.

1. 10 Skills To Look For When Making Your Next AI Hire (Hint: It Is Not Just About Coding Anymore)