Applying Artificial Intelligence to Automate Resume Screening in The Technology Sector
Abstract
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.
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References
- Bevara, R. V. K., Mannuru, N. R., Karedla, S. P., Lund, B., Xiao, T., Pasem, H., Dronavalli, S. C., & Rupeshkumar, S. (2025). Resume2Vec: Transforming applicant tracking systems with intelligent resume embeddings for precise candidate matching. Electronics, 14(4), 794. https://doi.org/10.3390/electronics14040794
- Clavié, B., & Soulié, G. (2023). Large language models as batteries-included zero-shot ESCO skills matchers. arXiv preprint arXiv:2307.03539. https://arxiv.org/abs/2307.03539
- Hamit, K., Serra-Vidal, M., & Wanner, L. (2025). Multilingual skill extraction for job vacancy–job seeker matching in knowledge graphs. In Proceedings of the Workshop on Generative AI and Knowledge Graphs (GenAIK) (pp. 146–155). International Committee on Computational Linguistics.
- Hunkenschroer, A. L., & Kriebitz, A. (2023). Is AI recruiting (un)ethical? A human rights perspective on the use of AI for hiring. AI and Ethics, 3, 199–213. https://doi.org/10.1007/s43681-022-00166-4
- Li, Q., & Lioma, C. (2024). Joint extraction and classification of Danish competences for job matching. arXiv preprint arXiv:2410.22103. https://doi.org/10.48550/arXiv.2410.22103
- Madanchian, M. (2024). From recruitment to retention: AI tools for human resource decision-making. Applied Sciences, 14(24), 11750. https://doi.org/10.3390/app142411750
- Magron, A., Dai, A., Zhang, M., Montariol, S., & Bosselut, A. (2024). JobSkape: A framework for generating synthetic job postings to enhance skill matching. In Proceedings of the First Workshop on Natural Language Processing for Human Resources (NLP4HR 2024) (pp. 43–58). Association for Computational Linguistics.
- Rigotti, C., & Fosch-Villaronga, E. (2024). Fairness, AI & recruitment. Computer Law & Security Review, 53, 105966. https://doi.org/10.1016/j.clsr.2024.105966
- Rosenberger, J., Wolfrum, L., Weinzierl, S., Kraus, M., & Zschech, P. (2025). CareerBERT: Matching resumes to ESCO jobs in a shared embedding space for generic job recommendations. Expert Systems with Applications, 275, 127043. https://doi.org/10.1016/j.eswa.2024.127043
- Werner, M., & Laber, E. (2024). Extracting section structure from resumes in Brazilian Portuguese. Expert Systems with Applications, 242, 122495. https://doi.org/10.1016/j.eswa.2023.122495