Internet memes have become a dominant and highly accessible medium for political discourse on social media. However, their multimodal nature—combining culturally specific visual symbols with code-mixed text—presents a significant challenge for automated content analysis, particularly in low-resource languages. In this study, we describe the system submitted by team RMS for the Multi-Level Political Meme Classification shared task at DravidianLangTech @ ACL 2026, focusing exclusively on the Tamil language track. We propose a robust late-fusion multimodal architecture that leverages a pre-trained ResNet-50 network for visual feature extraction and a Transformer-based model (MuRIL) for processing code-mixed Tamil text. The modalities are aligned using bidirectional cross-modal attention and combined using a Gated Multimodal Unit, allowing the model to dynamically weight the importance of visual versus textual cues. Our system ranked 11th on the official leaderboard with a macro-averaged F1-score of 0.7382. Through detailed error analysis, we demonstrate that while our gated fusion approach excels at identifying explicit trolling stances, it struggles with complex target resolution when visual and textual cues contradict.