AI / ML Engineer

Data Collection : Gather, preprocess, and clean data from various sources to create datasets suitable for training machine learning models. Feature Engineering : Select, extract, and engineer relevant features from data to improve model performance. Model Selection : Choose appropriate machine learning algorithms and models based on the problem at hand, such as regression, classification, clustering, or deep learning models. Model Training : Train machine learning models using historical data and employ techniques like supervised, unsupervised, and reinforcement learning. Hyperparameter Tuning : Optimize model performance by adjusting hyperparameters and fine-tuning the model to achieve the best results. Evaluation : Assess model performance using various metrics and techniques, such as cross-validation, ROC curves, and confusion matrices. Deployment : Deploy machine learning models into production systems, including web applications, mobile apps, or IoT devices. Continuous Improvement : Monitor model performance in real-world applications and retrain models as necessary to adapt to changing data patterns.