Author(s): Anubhav

Originally published on Towards AI.

It’s not the skills gap.

If you have applied to AI roles recently and heard nothing back, here is what is happening on the other side.

After the lead, the article argues that the market isn’t truly “not hiring”—it’s filtering for signal on the entry side. Recruiters and hiring managers don’t trust generic AI portfolios or take-home artifacts because they look like anything a subscription user can generate; instead, they look for candidates whose applications map to coherent role “rubrics” and whose work demonstrates real architectural judgment and “evaluation literacy.” The author explains that hiring filters focus on technical taste (making reliable, context-appropriate decisions) and eval-driven accountability (tests for grounding, regressions, drift, and hallucinations). To stand out, candidates should build “signed artifacts”: shipped agents tied to real users, wired through modern tool standards (like MCP) with clear security boundaries, backed by automated regression/evaluation suites, and documented with known failure modes and mitigations. The piece concludes with a practical transition plan and a rationale for why studying or tutorial projects no longer carry the same hiring signal—the receipt that still reads as human is evidence of real operation, failure, and repair.

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Published via Towards AI