Ranking individuals by relevance is central to many decision-making systems, such as hiring. A common approach is top-k ranking, where only the best k individuals are selected. However, social biases in data can make automated ranking systems discriminatory and affect individuals’ lives. In line with the AI Act, fair top-k ranking has recently gained attention. In this work, we focus on positional fairness in top-k rankings, which ensures equal exposure of groups across ranking positions. We formulate this as a constrained optimization problem that balances positional fairness with ranking quality. We propose two novel exact algorithms with optimal guarantees, along with an efficient greedy approximation. All methods support non-binary and multiple protected attributes as often present in real-world. Experiments on synthetic and real-world datasets show that our methods outperform recent approaches in both positional fairness and ranking quality.