Recommendations are a crucial part of the Netflix experience, and the company has been using complex production models that rely on thousands of hand-crafted features and specialized architectures. However, these models are costly to maintain and update, and the company is looking for more efficient and effective solutions. Large language models have shown promise in this area, but they are not yet production-ready and often have limitations such as over-recommending popular content and ignoring business constraints. To address this, Netflix built GenRec, an LLM-backed recommendation ranker that post-trains an internal foundation LLM on Netflix-specific data and objectives. GenRec verbalizes user histories, item metadata, and context as text and uses a catalog-aware scoring head to rank items. The model is trained with a multi-objective loss that combines ranking, language, and reward signals to align with business goals and long-term member satisfaction. GenRec has shown statistically significant improvements in both short-term and long-term online metrics compared to a well-tuned production ranker, while using only a small fraction of the labeled data and input signals. The model is served on Netflix's internal LLM stack using vLLM, and the company has implemented strategies to control serving cost, such as using smaller models, aggressive context compaction, and prefill-only inference. Overall, GenRec represents a significant step towards a more LLM-centric future for recommendation at Netflix, and its success has the potential to transform the way the company approaches recommendation and personalization. The model's ability to learn from natural language inputs and generate personalized rankings has the potential to improve the user experience and increase engagement. By leveraging the power of large language models, Netflix can create more effective and efficient recommendation systems that drive business success.