Chess engines represent a critical benchmark in artificial intelligence due to the game s complexity and well-defined rules. This project aimed to develop a functional and competitive chess playing engine, Illumina, employing the Alpha-Beta pruning algorithm to search the game tree and an Efficiently Updatable Neural Network (NNUE) to perform static evaluation of positions. The development of the engine was iterative, with SPRT testing steps on each newly integrated feature to statistically ensure strength progression or non-regression. The neural network employed for static evaluation was trained on data generated by millions of Illumina s self-play games in very fast time control. As of the writing of this paper, Illumina has been tested against several other engines and ranks highly among many other superhuman chess engines.