Vehicle re-identification is a procedure for identifying a vehicle using multiple non-overlapping cameras. The use of licence plates for re-identification have constraints because a licence plates may not be seen owing to viewpoint differences. Also, the high intra-class variability (due to the shape and appearance from different angles) and small inter-class variability (due to the similarity in appearance and shapes of vehicles from different manufacturers) make it more challenging. To address these issues, we have proposed a novel PATReId, Pose Apprise Transformer network for Vehicle Re-identification. This network works two-fold: 1) generating the poses of the vehicles using the heatmap, keypoints, and segments, which eliminate the viewpoint dependencies, and 2) jointly classify the attributes of the vehicles (colour and type) while performing ReId by utilizing the multitask learning through a two-stream neural network-integrated with the pose. The vision transformer and ResNet50 networks are employed to create the two-stream neural network. Extensive experiments have been conducted on Veri776, VehicleID and Veri Wild datasets to demonstrate the accuracy and efficacy of the proposed PATReId framework.