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.
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.
An AI product manager decides what AI-powered features to build and how to judge them, balancing user needs with model capabilities, data, evaluation and risk. They define success when outputs are probabilistic rather than fixed.
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.
Retrieval-augmented generation (RAG) is a pattern that hands a language model relevant passages from your own documents at question time. Its answers then rest on that material, not only on what it learnt in training.
Artificial intelligence (AI) is the field of building computer systems that can perform tasks that normally require human intelligence. These include understanding language, recognising images, making predictions and deciding what to do next.
A prompt engineer designs, tests and refines the instructions and context given to AI models so they produce accurate, consistent and safe output. For buyers, the key question is whether that work needs a dedicated role or sits within AI engineering.
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