The physics of nuclear reactions embodies essential information for the design, operation and decommissioning of nuclear systems, with applications spanning energy, safety, medicine, science, security and a great number of other industrial processes. Nuclear data represents our best knowledge of the physics of nuclear reactions, including its uncertainty. This project considers the design and software implementation of a nuclear data evaluation pipeline applied for a fully reproducible evaluation of neutron-induced cross sections using the nuclear model code TALYS. In particular, a unified representation of experimental data, systematic and statistical errors, model parameters and defects enables the application of the Generalized Least Squares method and its natural extension, the Levenberg-Marquardt (LM) algorithm, on a large collection of experimental data. The LM algorithm tailored to nuclear data evaluation takes into account the exact non-linear physics model to determine best estimates of nuclear quantities. The nuclear physics model contains 150-200 free parameters which are fitted to experimental data. Prior knowledge on the distribution of these parameters are handled in a Bayesian statistical model. Because of the computational demand of the nuclear physics model and the relatively large number of parameters the use of a high performance computing cluster becomes a necessity.