We present EROL, a multilingual neural machine translation system for four English–Indic language pairs: English↔Assamese, English↔Bodo, English↔Manipuri (Bengali script), and English↔Manipuri (Meitei Mayek). Our systems are built on the Transformer-Base architecture with 6 encoder and 6 decoder layers, a 512-dimensional model size, 2048-dimensional feed-forward networks, and 8 attention heads. We employ shared source and target embeddings, GELU activation, layer normalization, dropout regularization, and byte-pair encoding (BPE) tokenization. The models are trained using the standard sequence-to-sequence cross-entropy objective, providing a simple yet effective baseline for multilingual translation in low-resource settings. Experimental results demonstrate competitive performance across all translation directions, achieving BLEU scores of 25.10 for Assamese→English, 23.50 for Bodo→English, 22.47 for Manipuri (Bengali)→English, 24.71 for Manipuri (Meitei Mayek)→English, 15.31 for English→Assamese, 14.25 for English→Bodo, 10.21 for English→Manipuri (Bengali), and 3.96 for English→Manipuri (Meitei Mayek).