A hybrid framework for aerodynamic shape optimization is presented. The framework implements a sequential procedure, exploration-then-exploitation, coupling Bayesian global optimization with an adjoint-based gradient method. Gradient-based methods efficiently refine a design but are inherently sensitive to the initial point and often converge to a local minimum. To address this limitation, the Bayesian phase first explores the design space globally. A well-performing design is identified and ser