Machine Translation (MT) in the legal domain presents substantial challenges due to its complex terminology, lengthy statutes, and rigid syntactic structures. The JUST-NLP 2025 Shared Task on Legal Machine Translation was organized to advance research on domain-specific MT systems for legal texts. In this work, we propose a fine-tuned version of the pretrained large language model (LLM) ai4bharat/indictrans2-en-indic-1B, a transformer-based English-to-Indic translation model. Fine-tuning was performed using the parallel corpus provided by the JUST-NLP 2025 Shared Task organizers.Our adapted model demonstrates notable improvements over the baseline system, particularly in handling domain-specific legal terminology and complex syntactic constructions. In automatic evaluation, our system obtained BLEU = 46.67 and chrF = 70.03.In human evaluation, it achieved adequacy = 4.085 and fluency = 4.006. Our approach achieved an AutoRank score of 58.79, highlighting the effectiveness of domain adaptation through fine-tuning for legal machine translation.