Every developer I've seen stumble when building recruitment AI made the same mistake at the start: they picked a model before they understood the hiring process they were building for. It's an easy trap. The ML problem feels well-defined — classification, ranking, matching. So you reach for embeddings, fine-tune a transformer, benchmark against a test set, and ship. Then the recruiter opens it, tries it on three real candidates, and the feedback comes back: this doesn't reflect how we actually hire. That gap between model performance and real-world hiring logic is where most recruitment AI quietly fails. Here's what to build your mental model around before writing a single line of code. Step 1: Resume Parsing and Data Structuring Recruitment data is genuinely messy in ways that synthetic datasets don't prepare you for. Resumes arrive as PDFs, Word docs, LinkedIn exports, and plain text — each with wildly inconsistent structure.…