This study investigates the presence and mitigation of gender bias in English-Ukrainian machine translation (MT) models. We focused on the transfer of gender bias in two English-Ukrainian MT models, using sentences that contained professional occupation names. We evaluated two gender bias mitigation methods: 1) gender tagging of source sentences, and 2) gender bias correction by Lapa LLM, a Ukrainian large language model (LLM), using a dataset that we curated for our evaluation. Our results showed that both zero-shot models contained English-Ukrainian gender bias transfer, particularly for gender-stereotypical occupations. The gender tagging mitigation method demonstrated changes to the gender assignment in our data; however, these changes led to mixed results in gender bias correction. The Lapa LLM-correction method had promising results in demonstrating a considerable mitigation of bias in our evaluation set. Overall, our study contributes a framework for the evaluation of gender bias in English-Ukrainian translation that could potentially be applied to translation pairs in other languages.