The paper examines the transition of applicant tracking systems (ATS) from record-keeping databases to intelligent decision-support platforms grounded in representation learning and modular architectures. The study synthesizes peer-reviewed findings on semantic resume–job matching, learning-to-rank pipelines, human-in-the-loop re-ranking, and governance practices for fairness and auditability. Particular attention is paid to latency-aware MLOps, API-first interoperability, and explanation surfaces that restore recruiter control while compressing screening cycles. The analysis aligns these capabilities with persistent U.S. hiring frictions—screening workload, time-to-shortlist, and cost-per-hire—showing where embedding-based triage and event-driven integration yield measurable improvements in throughput and shortlist quality. The article proposes a product blueprint: fidelity-preserving parsing, domain-tuned encoders, hybrid re-rankers, continuous bias and drift monitoring, and evented integration with enterprise HR stacks. The discussion outlines risk–control mappings (bias, drift, opacity, load) and operational metrics for evaluation. Findings inform platform designers, HR leaders, and policy stakeholders seeking accountable automation that reduces delay while improving match quality.

applicant tracking systems, talent acquisition, resume–job matching, sentence embeddings, learning-to-rank, explainability, bias mitigation, MLOps, interoperability, human-in-the-loop

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