Machine Learning Engineers (alias for the ML side of the AI/ML split — see also ai-engineer) train, productionize, and monitor ML systems at scale. They own the full lifecycle: feature pipelines, model training, deployment, monitoring, and incremental retraining. The work spans classical ML (recommender systems, fraud, ranking) and deep learning, with strong infrastructure ownership.