Modeling user action sequences has become a popular focus in industrial recommendation system research, particularly for Click-Through Rate (CTR) prediction tasks. However, industry-scale CTR models often rely on short user sequences, limiting their ability to capture long-term behavior. They also rarely address the infrastructure challenges involved in efficiently serving large-scale sequential models. Additionally, these models typically lack an integrated action-prediction task within a point-wise ranking framework, reducing their predictive power. We introduce TransAct V2, a production model for Pinterest's Homefeed ranking system, featuring three key innovations: (1) leveraging very long user sequences to improve CTR predictions, (2) employing scalable, low-latency deployment solutions tailored to handle the computational demands of extended user action sequences, and (3) integrating a Next Action Loss function for enhanced user action forecasting. To overcome latency and storage constraints, we leverage efficient data-processing strategies and model-serving optimizations, enabling seamless industrial-scale deployment. Our approach's effectiveness is further demonstrated through ablation studies. Furthermore, extensive offline and online A/B experiments confirm major gains in key metrics, including engagement volume and recommendation diversity, showcasing TransAct V2's real-world impact.