AI engineers involve themselves with data at all points of AI system development. They gather raw data from different sources, preprocess it to eliminate discrepancies, and shape it into appropriate structured forms compatible with machine learning models. Feature engineering, through the process of choosing and enhancing relevant data properties, is one of the fundamental aspects of their job. AI engineers also coordinate with data analysts and scientists for interpreting insights as well as polishing datasets for higher model performance.
Outside model training, AI engineers manage data pipelines to maintain real-time processing of data to support AI for industries such as healthcare, finance, and automation. They also use big data technologies and cloud computing to make large datasets easily manageable. In addition, they work on ethics in AI through the mitigation of biases in the data and providing fairness in machine learning models. Their ability to manage data properly decides the correctness, reliability, and resilience of AI-based solutions, so data management becomes an integral part of AI engineering.