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 NLP engineer builds software that understands, extracts from or generates human language, using classic NLP and large language models. They turn text and speech data into reliable product features.
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 feature store is a system that stores, documents and serves the input features used by machine learning models. It keeps them consistent between training and live predictions.
DevOps automates building, testing and running code, while MLOps extends that practice to the data, training and evaluation that machine learning models need. MLOps also tracks model drift after release, a problem ordinary application code does not face.
Machine learning is a branch of AI in which systems learn patterns from data to make predictions or decisions. The behaviour comes from training on examples rather than from rules written by hand.
A Python developer writes software in Python, a general-purpose language used for web backends, automation, data pipelines, machine learning and scripting. Before hiring one, decide which of those areas the job actually needs.
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