This paper details the process for designing both a simulation of the board game Jaipur, and an artificial intelligence (AI) agent that can play the game against a human player. When designing an AI for a card game, there are two major problems that can arise. The first is the difficulty of using a search space to analyze every possible set of future moves. Due to the randomized nature of the deck of cards, the search space rapidly leads to an exponentially growing set of potential game states to analyze when one tries to look more than one turn ahead. The second aspect that poses difficulty is the element of uncertainty that exists from opponent feedback. Certain moves are weak to specific opponent reactions, and these are difficult to predict due to hidden information. To circumvent these problems, the AI uses a greedy approach to decision making, attempting to maximize the value of its plays immediately, and not play for future turns. The agent utilizes conditional statements to evaluate the game state and choose a game action that it deems optimal, a heuristic to place an expected value (EV) of the goods it can choose from, and selects the best one based on this evaluation. Initial implementation of the simulation was done using C++ through a terminal application, and then was translated to a graphical interface using Unity and C#.