Solving linear systems is critical in enabling large-scale implicit nonlinear solid mechanics applications. These linear systems can be solved with direct or iterative solution algorithms which provide the direction to a nonlinear solution algorihthm (typically Newton's method, Arclength methods, and/or Nonlinear conjugate gradient). The dominant cost in these types of computations is the linear system assembly and solve. This work explores using domain decomposition algorithms (FETI-DP) as the preconditioner to a conjugate gradient iterative algorithm to improve efficiency and robustness for an Implicit Nonlinear solid mechanics application. A series of increasingly complex nonlinear solid mechanics application problems will be used to demonstrate the efficiency and robustness of the proposed algorithm. Detailed discussion on software design towards providing flexibility in preconditioner choice will also be discussed.