Decomposing the structure of a large number of existing posts through data mining will greatly improve the effect of enterprise human resource structure optimization. To this end, this paper proposes an end-to-end competency-aware job requirement generation framework to automate the job requirement generation, and the prediction based on competency themes can realize the skill prediction in job requirements. Then an encoder-decoder LSTM is proposed to implement job requirement generation, and a competency-aware attention mechanism and a replication mechanism are proposed to guide the generation process to ensure that the generated job requirement descriptions comprehensively cover the relevant and representative competency and job skill requirements. A competency-aware strategy gradient training algorithm is then proposed to further enhance the rationality of the generated job requirement descriptions. Finally, extensive experiments on real-world HR data sets clearly validate the effectiveness and interpretability of the proposed framework and its variants compared to state-of-the-art benchmarks.