ML Engineer
The Machine Learning Engineer (ML Engineer) is responsible for turning a machine-learning model (often prototyped by a Data Scientist) into a robust production system: packaging, deployment, monitoring, updates.
The [Machine Learning](/ressources/glossaire-de-la-tech/machine-learning) Engineer (ML Engineer) is responsible for turning a machine-learning model (often prototyped by a [Data Scientist](/ressources/glossaire-de-la-tech/data-scientist)) into a robust production system: packaging, deployment, monitoring, updates.
They combine Data Scientist skills (statistics, modelling) with Software Engineer skills (solid Python, tests, CI/CD, containers, cloud). They typically know the major ML frameworks (PyTorch, TensorFlow, scikit-learn), MLOps tools (MLflow, Weights & Biases, Vertex AI, SageMaker) and the basics of data engineering.
Not to be confused with an AI Engineer (more LLM application-focused) or an MLOps Engineer (more infrastructure-focused).