Arabic machine translation remains challenging because Modern Standard Arabic and dialectal varieties such as Egyptian Colloquial Arabic differ substantially in morphology, lexical choice, register, and everyday usage. We introduce a compact Arabic-centric encoder–decoder machine translation model designed to strengthen translation involving Modern Standard Arabic and Egyptian Arabic while preserving multilingual translation ability. We pretrain from scratch with multilingual Seq2Seq training and contrastive cross-lingual alignment, using an Arabic-centered data mixture covering Arabic, English, Egyptian Arabic, and 111 additional languages. We further apply Direct Preference Optimization (DPO) for our Seq2Seq model using grounded preference pairs targeting Arabic-specific translation errors, including literal translation, register mismatch, lexical ambiguity, domain terminology, and dialectal naturalness. We evaluate our model on FLORES, WMT24++, and NTREX using spBLEU. Results show that our model remains competitive with substantially larger LLM baselines and achieves its clearest gains on Arabic-centered directions, especially Egyptian Arabic. DPO improves FLORES translation into English by approximately +2 spBLEU for Egyptian Arabic and +2 spBLEU for Modern Standard Arabic. Our findings support targeted Arabic-centric Seq2Seq modeling as an efficient path for dialect-sensitive machine translation.