A Model Evaluation and Validation developer is responsible for assessing and fine-tuning machine learning models to ensure their efficiency and accuracy. They split data into training and testing sets, apply various evaluation metrics like precision, recall or F-score, and run cross-validation to verify model performance. They validate the models using techniques like k-fold cross-validation, stratified k-fold, or time series cross-validation. They also address issues of overfitting or underfitting by adjusting model parameters or using regularization techniques. Their goal is to optimize the model's predictive capabilities while minimizing the risk of errors or bias.