The purpose of this master’s thesis was to develop artificial intelligence capable of playing chess (chess engine) and incorporate it into a microservices architecture web application, enabling users to play chess against a computer and analyse chess games. The chess engine was implemented using the minimax algorithm with alpha-beta pruning in the C++ programming language. Given that the game’s logic, move generation, and position evaluation require a significant number of computational operations, C++ was chosen due to its well-known speed compared to other modern object-oriented programming languages. In order to boost the efficiency of position evaluation, apart from heuristic functions, the implementation included various optimizations, such as Zobrist hashing. Apart from evaluating positions, the entire set of rules and chess logic, along with the generation of all potential moves within specified positions, were also implemented. To optimize memory usage and the search for possible moves, the bitboard principle was employed. This principle stores information about pieces and their positions in binary form within 64 bits, replacing time-consuming operations with classic logical operations. The thesis presents the results of the described optimizations numerically and visually at various evaluation depths. For application development, technologies such as Express.js, the Node.js web framework, React.js, PostgreSQL, MongoDB, and others were utilized. ZeroMQ was used to enable communication between Docker containers and exchange messages between the engine and the back-end.