The article examines how modern AI pipelines automate first-pass résumé screening for technology roles while preserving recruiter control and legal defensibility. Reproducible evidence from recent peer-reviewed and preprint studies shows that structure-aware parsing stabilizes downstream extraction, domain-adapted encoders raise retrieval quality over keyword ATS baselines, and multilingual, taxonomy-anchored skill extraction reduces manual curation for heterogeneous applicant pools. Governance syntheses specify safeguards—audits, calibrated thresholds, span provenance, and contestability—suitable for large employers. An overlay architecture is derived that integrates with existing ATS workflows without occluding the résumé, uses embedding-first retrieval with lightweight re-ranking, and logs interpretable artifacts for audits. Industry benchmarks on time-to-fill, cost-per-hire, and screening effort motivate the intervention and calibrate expected cycle-time reductions and quality-of-hire gains for U.S. tech hiring. The contribution is a consolidated, deployment-oriented blueprint that connects empirical gains in ranking and extraction with organizational guardrails and recruiter-facing design choices.

AI in hiring, résumé parsing, skills extraction, ESCO/O*NET, transformer embeddings, candidate–job matching, fairness auditing, explainable screening, Applicant Tracking Systems, technology sector

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