An AI engineer builds products on top of existing models, such as large language models, while an ML engineer trains, tunes and serves models from data. Which to hire depends on whether you need to apply models or create them.
A large language model (LLM) is a neural network trained on very large amounts of text to predict and generate language. It powers chat assistants, summarisers, coding tools and AI agents.
MLOps is the practice of deploying, monitoring and updating machine learning models in production. It applies DevOps discipline to models and the data they depend on, so releases are repeatable, auditable and reversible.
A machine learning engineer builds, deploys and maintains machine learning models as reliable software in production. Teams usually hire one once a model has proved its value and must run, scale and stay accurate every day.
An AI engineer builds software products and features on top of AI models, especially large language models, using prompting, retrieval, tool use and evaluation. Companies hire one to turn a promising AI demo into a dependable product.
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