The detection and rapid characterisation of earthquake parameters such as magnitude are important in real time seismological applications such as Earthquake Monitoring and Earthquake Early Warning (EEW). Traditional methods, aside from requiring extensive human involvement can be sensitive to signal-to-noise ratio leading to false/missed alarms depending on the threshold. We here propose a multi-tasking deep learning model – the Convolutional Recurrent model for Earthquake Identification and Magnitude Estimation (CREIME) that: (i) detects the earthquake signal from background seismic noise, (ii) determines the first P-wave arrival time and (iii) estimates the magnitude using the raw 3-component waveforms from a single station as model input. Considering, that speed is essential in EEW, we use up to two seconds of P-wave information which, to the best of our knowledge, is a significantly smaller data window compared to the previous studies. To examine the robustness of CREIME we test it on two independent datasets and find that it achieves an average accuracy of 98\% for event-vs-noise discrimination and can estimate first P-arrival time and local magnitude with average root mean squared errors of 0.13 seconds and 0.65 units, respectively. We compare CREIME with traditional methods such as short-term-average/ long-term-average (STA/LTA) and show that CREIME has superior performance, for example, the accuracy for signal and noise discrimination is higher by 4.5\% and 11.5\% respectively for the two datasets. We also compare the architecture of CREIME with the architectures of other baseline models, trained on the same data, and show that CREIME outperforms the baseline models.