In the evolving realities of recruitment, the precision of job–candidate matching is crucial. This study explores the application of Zero-Shot Recommendation AI Models to enhance this matching process. Utilizing advanced pretrained models such as all-MiniLM-L6-v2 and applying similarity metrics like dot product and cosine similarity, we assessed their effectiveness in aligning job descriptions with candidate profiles. Our evaluations, based on Top-K Accuracy across various rankings, revealed a notable enhancement in matching accuracy compared to conventional methods. Specifically, the all-MiniLM-L6-v2 model with a chunk length of 768 exhibited outstanding performance, achieving a remarkable Top-1 accuracy of 3.35%, 55.45% for Top-100, and an impressive 81.11% for Top-500, establishing it as a highly effective tool for recruitment processes. This paper presents an in-depth analysis of these models, providing insights into their potential applications in real-world recruitment scenarios. Our findings highlight the capability of Zero-Shot Learning to address the dynamic requirements of the job market, offering a scalable, efficient, and adaptable solution for job–candidate matching and setting new benchmarks in recruitment efficiency.

Job–candidate matching is pivotal in recruitment, yet traditional manual or keyword-based methods can be laborious and prone to missing qualified candidates. In this study, we introduce the first Siamese framework that systematically contrasts GRU, LSTM, and Transformer sequential heads on top of a multilingual Sentence Transformer backbone, which is trained end-to-end with triplet loss on real-world recruitment data. This combination captures both long-range dependencies across document segments and global semantics, representing a substantial advance over approaches that rely solely on static embeddings. We compare the three heads using ranking metrics such as Top-K accuracy and Mean Reciprocal Rank (MRR). The Transformer-based model yields the best overall performance, with an MRR of 0.979 and a Top-100 accuracy of 87.20% on the test set. Visualization of learned embeddings (t-SNE) shows that self-attention more effectively clusters matching texts and separates them from irrelevant ones. These findings underscore the potential of combining multilingual base embeddings with specialized sequential layers to reduce manual screening efforts and improve recruitment efficiency.

Recruitment automation increasingly relies on Large Language Models (LLMs) for extracting structured information from unstructured CVs and job postings. However, production data often arrive as heterogeneous, privacy-sensitive PDFs, limiting reproducibility and compliance. This study introduces a deterministic, GDPR-aligned pipeline that converts recruitment documents into structured, anonymized Markdown and subsequently into validated JSON ready for downstream AI processing. The workflow combines the Docling PDF-to-Markdown converter with a two-pass anonymization protocol and evaluates three LLM back-ends—GPT-4o (Azure, frozen proprietary), GPT-OSS-120B and Llama-3.1-8B-Instruct—using identical prompts and schema constraints under near-zero-temperature decoding. Each model’s output was assessed across 2280 multilingual CVs using two complementary metrics: reference-based completeness and content similarity. The proprietary GPT-4o achieved perfect schema coverage and served as the reproducibility baseline, while the open-weight models reached 73–79% completeness and 59–72% content similarity depending on section complexity. Llama-3.1-8B-Instruct performed strongly on standardized sections such as contact and legal, whereas GPT-OSS-120B better-handled less frequent narrative fields. The results demonstrate that fully deterministic, auditable document extraction is achievable with both proprietary and open LLMs when guided by strong schema validation and anonymization. The proposed pipeline bridges the gap between document ingestion and reliable, bias-aware data preparation for AI-driven recruitment systems.