Non-Intrusive Load Monitoring (NILM) is the task of decomposing aggregate power consumption into individual appliance-level signals. Existing deep learning approaches for NILM typically address either appliance state detection or power estimation as separate tasks, limiting their practical utility. This paper proposes UNet-NILM, a multi-task deep neural network architecture inspired by the U-Net encoder-decoder design for semantic segmentation. UNet-NILM simultaneously detects appliance ON/OFF states and estimates per-appliance active power consumption from aggregate mains readings. We evaluate the approach on the UK-DALE and REFIT datasets across five target appliances. Results show that UNet-NILM achieves state-of-the-art performance on both tasks simultaneously, demonstrating that joint optimisation of detection and estimation objectives is mutually beneficial. The proposed architecture offers a practical step toward deployable NILM systems that provide both appliance status and fine-grained energy consumption data.