If you’re trying to get a job in AI, you’ve already heard that you’re supposed to build projects. The hard part is that there’s a huge gap between following a tutorial where somebody else has already made every decision, and building a legit project from scratch.
So in this guide I’m going to walk you through the actual process I use to build AI systems, from figuring out what’s worth building in the first place, all the way through testing, deployment, and leveraging it for job opportunities once it’s live.
This is the same basic process I’ve used building production AI and ML systems at companies like Amazon, but I’m going to break down every step assuming you’ve never built an AI product before.
The workflow at a glance
Know why you’re building. Learning, demonstrating skills, or getting real users.
Pick a problem you understand. Then work backwards to the technology.
Scope it before you build. Who it’s for, constraints, data, success metrics, and whether AI even makes sense.
Work with AI the right way. Spec-driven development, so you make the decisions, not Claude.
Set up the foundations. Data storage, API keys, environments.
The part about defining the problem before choosing the technology really stood out to me. I also liked the idea of keeping decisions in files instead of letting important context disappear into chat history. That small change makes the whole AI-assisted development workflow feel much more intentional.
