In recent years, generative artificial intelligence (Gen AI) systems have assumed increasingly crucial roles in selection processes, personnel recruitment and analysis of candidates’ profiles. However, the employment of large language models risks reproducing, and in some cases amplifying, gender stereotypes and bias already present in labour market. This research aims to evaluate and measure this phenomenon, analysing how a state-of-the-art generative model (ChatGPT-5) suggests occupations and represents the ideal candidate based on gender and work experience background, focusing on under 35 years old Italian graduates. The study is organised into two complementary phases. In Phase I, the model has been trained to provide job suggestions to 24 simulated candidates profiles of female and male genders, balanced in age, experience and professional field.