Almost every community Question-Answering (cQA) platform has the pressing need of enhancing user experience by presenting dedicated displays, connecting potential answerers with open questions and revitalizing the material in their archives. In doing so, it is crucial to understand the profile of their community members, especially as it relates to their demographics. In this realm, variables such as age and gender have shown to be particularly promising for managing content. For instance, they make it easier to connect questions posted by one generation that are more likely to be answered by individuals from the previous generation. This paper advances the current body of knowledge in this area by exploring the performance of nineteen frontier transformer-based models (e.g., BERT and ELECTRA) on age recognition across a large-scale collection of cQA members. In effect, the best encoder (LongFormer) finished with an accuracy of 78.61% (F1-Score of 0.7424) by taking full-questions and answers into account. Unlike gender recognition, our outcomes do not show a noticeable difference between cased and uncased models. But on the other hand, they confirm that the transition from one age group to the other is smooth, and thus boundary individuals pose a tough challenge to discriminant models built on top of frontier machine learning approaches.