Transferring Tech Recruiting Heuristics to Creative Engineering Domains: A Hybrid Skill Taxonomy Model for Sourcing Top-Tier Designers and Developers

Abstract

Few candidates will ever squeeze their elite designers and developers into conventional recruitment funnels; non-technical recruiters are especially found wanting in sourcing for creative engineering specialities. Herein, we present a hybrid skills framework enhanced by AI tools that enable recruiters to make confident hiring decisions without requiring in-depth domain knowledge. By merging algorithmic evaluation of candidate work products from GitHub, Figma, and Behance with natural language processing of voice-interview transcripts, our approach converts a subjective experience into a reproducible process driven by objective metrics. At its core lies the SNRHiring Index, a proprietary composite score computed from hard skills and behavioural stimuli verified against real-world digital signals. Built with scalability and transparency in mind, this framework has since been applied in a production setting for tech recruitment, reducing hiring time, increasing the quality-to-fit rate of portfolios, and improving post-hire retention. The conclusions, therefore, enable the building of a repeatable playbook for HR practitioners, startup CTOs, and HRTech researchers interested in innovating creative technical hiring. By affording clients the ability to source high-quality talent even without insider knowledge, this paper thus contributes not only to the AI-hiring literature but also to establishing a DOI-citable proof of method for platform credibility, investor assurance, and transparency to clients.