Vaibhav Pandey is a researcher and practitioner in AI-driven cloud computing, reinforcement learning, and large-scale systems optimization. He has published in IEEE and Springer, including work on predictive auto-scaling and reinforcement learning–based cost and performance optimization in Kubernetes. His research has been presented at IEEE SoutheastCon and the International Conference on P2P, Parallel, Grid, Cloud and Internet Computing (Springer, 2025). In addition to academic contributions, he has authored industry articles in Enterprise Executive and IEEE Computer Society Tech News. Vaibhav also serves as a peer reviewer for IEEE Computer Magazine and on program committees for conferences including ECAI 2025 and IEEE ICMRACC 2025.