In hybrid manufacturing (i.e., combined additive and conventional manufacturing), one notable use case for structural applications involves using additive manufacturing to fabricate stiffeners. Wire and Arc Additive Manufacturing (WAAM) is leveraged to print stiffeners onto base geometries, which mainly consist of already manufactured sheet‑metal components that are dimensionally fit for existing assemblies. Adding stiffeners enables increasing the stiffness for targeted use cases without redesigning the part, thus reducing lead times and cost. Hence, the goal is to optimize stiffeners based on existing geometries. This causes challenges for the use of FEM-based structural optimization: when stiffeners and base geometry are coupled by sharing nodes, any stiffener design update perturbs the base mesh. One established way to couple non-conforming meshes is using Lagrange multipliers. However, this requires exact knowledge of element shape functions, typically unavailable in commercial software due to proprietary restrictions. To circumvent this, we introduce a coupling method deployable in commercial FEM environments without requiring knowledge of shape functions, i.e., non-intrusive, where the base mesh is locally subdivided only at elements intersected by stiffener nodes. The stiffener shape and size optimization is driven by vertex morphing, enabling smooth, controllable geometric updates along the stiffener path. The optimization incorporates a set of constraints spanning WAAM-specific manufacturing constraints (e.g., bead spacing, width, height), global packaging to ensure assembly fit, and mass/volume limits to cap added weight. Beyond academic use cases, the approach is applied to a real‑part geometry with complex curvature and local features. This optimization framework is also designed to be extended with coupled transient thermo‑mechanical analysis, where sensitivities are computed to capture how local shape and size variations influence peak temperatures, and, consequently, thermal displacements, so that future gradient‑based optimization is informed by these sensitivities, further aligning design outcomes with the WAAM process. The ultimate objective is to simultaneously improve structural performance while ensuring manufacturability and assemblability.