Machine Learning engineer: complete definition in AI for SMEs

A Machine Learning (ML) engineer is the specialist who designs, trains, deploys and maintains AI models in production. Where a data scientist explores and experiments, the ML engineer industrializes: they turn a model prototype into a reliable, scalable and maintainable service, accessible via an API or integrated into a product.

What it changes for an SME

For an SME, the ML engineer steps in when the need goes beyond a Python script and an LLM API:

• Custom model: training a document classification model with your business categories, not a generic model's;
• On-premise deployment: running a Llama or Mistral model internally, on a GPU server, to keep data secure;
• Cost optimization: reducing model size to serve more requests at the same budget;
• Monitoring: watching for performance drift in a production model (data changes, model drift).

When it is needed

Most SME AI use cases do not require an ML engineer — an OpenAI API plus a good workflow suffice. The ML engineer becomes relevant when volumes are high, confidentiality is strict, or the business need is too specific for a generic model. In fractional AI leadership, we make that choice based on the use case, not on technical sophistication.