Anomaly detection on tabular data is critical in finance and cybersecurity, where graph structures are unavailable or unreliable. However, building effective detection models often requires labor-intensive feature engineering, model selection, and ensemble design, limiting scalability and robustness. To address this, recent work has explored automated machine learning (AutoML) as a means of streamlining the end-to-end modeling process. Despite progress, existing AutoML frameworks face structural limitations. They typically discard features based on single-model performance, include untuned models in ensembles, and fail to account for the quality of each model in final voting. These issues are especially problematic in anomaly detection, where heterogeneous patterns and varying anomaly types demand both high feature diversity and robust decision mechanisms. We propose FLEX-AD (Feature-Level and Exitable Ensemble eXploration for Anomaly Detection), a two-stage AutoML framework tailored for tabular anomaly detection. FLEX-AD first uses large language models (LLMs) to iteratively generate candidate feature engineering code and evaluates features across base models, retaining those that improve performance on any. In the second stage, each model undergoes grid-based hyperparameter tuning ranked by validation performance. Top models are selected for weighted soft-voting, ensuring reliable ensemble decisions. Experiments on 19 real-world datasets show that FLEX-AD achieves superior performance over existing baselines, especially in clustering with up to 8% ARI gain, offering a scalable, robust solution for tabular anomaly detection.