For tech hopefuls trying to break into the industry, demonstrating proficiency with artificial intelligence (AI) is no longer optional. While AI integration across most roles and areas of business is still in its early stages, it has fast become the buzzword hiring managers expect every resume to address.
However, that can pose a dilemma for early-career candidates who haven’t worked in AI-specific roles or who have limited IT experience. To put it another way, how do you demonstrate AI know-how when your job history doesn’t say “machine learning engineer”?
Ashwin Ballal, CIO at Freshworks, explains you don’t necessarily need formal experience in an AI-specific role to stand out; what you do need to demonstrate is curiosity, critical thinking, and the capacity to apply AI meaningfully.
“If someone can demonstrate how they’ve used AI-powered tools to work more efficiently, or an understanding of how AI tools can help automate a task and improve a workflow, that’s a good sign for their working style,” he says.
That mindset—outcome-oriented and practical—is echoed by Ornella Casagrande Rizzi, head of learning and development at Indicium: “Even if you haven’t had the chance to work in an AI-specific role, there are still plenty of ways to show that you understand the field and can apply its concepts.”
She points to hands-on learning as a great place to start.
“Whether it is building a simple AI tool, experimenting with open-source models, or joining beginner-friendly challenges online, these kinds of projects show that you’re not just learning theory but also applying it in real ways,” Rizzi says.
Entry-level professionals shouldn’t get hung up on sophistication. “What tends to make a beginner AI project stand out is not necessarily its complexity, but the clarity of thought and intention behind it,” she adds.
That means it’s perfectly fine to work on small projects—if they’re purposeful, show initiative, and solve a real-world problem.
Another tip: Don’t just build in silence but document your process. Employers want to see how you learn, not just what you built.
“A well-explained learning process, one that includes the challenges faced, the decisions made along the way, and reflections on what could be improved, can be incredibly valuable,” Rizzi says.
Courses, Certifications, Projects and Portfolios
While courses and certifications demonstrate effort (CompTIA AI Essentials is one such foundational course), Ballal says they’re not enough on their own.
“Courses are a great start, but employers want to see what you do with what you’ve learned,” Ballal says.
If the course includes a project component, that’s great; but if it doesn’t, consider building your own. This will demonstrate initiative, curiosity, and problem-solving instincts.
Then comes the portfolio, which offers you the chance to showcase what you’ve done, whether it’s code samples or tool integrations. “If you can articulate what problem you were solving, why AI was the right tool, and what value it created—whether through screenshots, video walkthroughs, or working prototypes—that’s gold,” Ballal says.
Rizzi takes it a step further: Your portfolio isn’t just a showcase of what you can do, it’s a narrative of how you think: “It’s far more impactful to present a few well-developed projects than to include a long list of disconnected ones.”
The main goal is to make your work history coherent, intentional, and evolving. This includes writing clearly about your decision-making process, even when things didn’t go according to plan.
Acing the Interview
During the interview process, it’s less about flexing complex jargon and more about demonstrating critical thinking capabilities. “AI is far more of an augmentation tool, a powerful assistant, or an agent rather than a replacement,” Ballal says.
He urges junior candidates to think critically about how AI fits into modern workflows and understanding how it enhances productivity.
Rizzi says junior candidates don’t need to have all the answers, but they do need to show that they’re genuinely interested in learning and actively paying attention to what’s happening in the field. That means reading about real-world use cases, understanding ethical concerns, and keeping up with how AI is being applied—not just how it’s being coded.
It’s also important not to treat your learning like a checklist. “A portfolio should be seen as something ongoing, something that evolves as you do,” Rizzi says. “Update it regularly, add reflections and show growth.”
In short, don’t just show what you know. Show how you think. By demonstrating that you know how to use AI—not just study it—you can prove your relevance in any role.
“Build something, even a small project, that shows how you’ve used AI to simplify or improve a process,” Ballal says. “In this era, less is more: strip away complexity and use technology to produce fast, tangible results.”
