The mass independent energy reconstruction of cosmic rays is crucial for understanding their origin, acceleration, and propagation. Precise measurement of the primary energy can also lead to better mass classification and could enable energy dependent anisotropy maps for individual elements. The GRAPES-3 experiment located in Ooty consisting of 400 scintillator detector array placed 8 m apart covering an area of 25000 m$^2$ with a dedicated muon detector made of 3712 proportional counters, is designed to do these kinds of measurements. Previously electron size calibration curves have been used to find primary energy in the GRAPES-3 data analysis framework however significantly better precision can be established using graph neural network. Thus, in this work we have implemented a modular and dynamic GNN based reconstruction algorithm that automates feature mapping. We demonstrate how the model is learning by studying its latent space and show that scaling the metric in the latent space can lead to further improvements in response resolution. Fine-tuned strategies are presented and a thorough comparison of the reconstructed energy and bias is done for different fine-tuned models along with studying the resolution variation for different mass groups and shower age.