We describe our submission to the shared task LoResMT 2026, which involved translating from low-resource Turkic languages Bashkir, Chuvash, Kazakh, Kyrgyz, and Tatar from English or Russian. We submitted runs for the English-Chuvash language pair using Neural machine translation (NMT). Our approach focused on systematic experimentation with diverse model architectures and an emphasis on optimizing inference-time parameters. The key findings indicate that a large-scale, specialized multilingual translation model, combined with targeted data preprocessing and careful generation tuning, yielded the best performance, achieving a chrF++ score of 29.67 on the public test set.