Classifying job offers into occupational categories is a fundamental task in human resource information systems, as it improves and streamlines indexing, search, and matching between openings and job seekers. Comprehensive occupational databases such as ${\text{O}}^{\ast }$NET or ESCO provide detailed taxonomies of interrelated positions that can be leveraged to align the textual content of postings with occupational categories, thereby facilitating standardization, cross-system interoperability, and access to metadata for each occupation (e.g., tasks, knowledge, skills, and abilities). In this work, we explore the effectiveness of fine-tuning existing language models (LMs) to classify job offers with occupational descriptors from ${\text{O}}^{\ast }$NET. This enables a more precise assessment of candidate suitability by identifying the specific knowledge and skills required for each position, and helps automate recruitment processes by mitigating human bias and subjectivity in candidate selection. We evaluate three representative BERT-like models: BERT, RoBERTa, and DeBERTa. BERT serves as the baseline encoder-only architecture; RoBERTa incorporates advances in pretraining objectives and data scale; and DeBERTa introduces architectural improvements through disentangled attention mechanisms. The best performance was achieved with the DeBERTa model, although the other models also produced strong results, and no statistically significant differences were observed across models. We also find that these models typically reach optimal performance after only a few training epochs, and that training with smaller, balanced datasets is effective. Consequently, comparable results can be obtained with models that require fewer computational resources and less training time, facilitating deployment and practical use.