Data Engineering and MLOps differ mainly in the stage of the AI workflow they support. Data Engineers focus on building and maintaining data infrastructure by designing data pipelines, performing ETL/ELT processes, managing data storage, and ensuring that data is clean, reliable, and accessible for analytics and machine learning. Their work often involves tools such as Apache Spark, Kafka, Hadoop, SQL databases, and cloud data platforms, along with strong skills in data modeling and distributed systems. MLOps, on the other hand, focuses on operationalizing machine learning models by automating model training, versioning datasets and models, deploying models into production, and monitoring model performance and drift. MLOps engineers typically use tools like MLflow, Kubeflow, Docker, Kubernetes, and CI/CD pipelines, requiring knowledge of machine learning workflows, automation, and cloud infrastructure. In real-world AI projects, Data Engineering ensures that high-quality data flows reliably into systems, while MLOps ensures that machine learning models are deployed, maintained, and continuously improved in production, enabling organizations to turn data into scalable and reliable AI-driven solutions.