From what I’ve seen and this comes up again and again companies in the U.S. aren’t all that impressed if your AI knowledge stays on paper. They’re usually more interested in what you’ve actually built or tried to build. Sure, you still need the basics: Python, a decent grasp of machine learning ideas, and some comfort working with data. That’s kind of the entry point.
Where things start to shift is with real, hands-on work. The projects you’ve put together, the not-so-clean datasets you’ve wrestled with, the moments where things didn’t quite work and you had to figure out why… that’s the stuff people tend to ask about. Even a small project can carry weight if you can explain your thinking without getting lost in jargon.
Communication plays a bigger role than most expect, too. You’re not always talking to other engineers sometimes it’s managers or clients who just want the “what” and “why” in plain terms. Being able to break things down clearly makes a difference.
That’s something programs like Ai Course Certification H2K Infosys seem to focus on. It’s not just theory-heavy learning they push you into practical scenarios so you’re actually applying what you pick up. Over time, that mix of trying, adjusting, and understanding starts to feel a lot closer to real work, which is kind of the whole point.