Prompt Engineer vs ML Engineer vs AI Engineer: Hiring the Right Role

As a standalone, full-time title, 'Prompt Engineer' has become less common than it was a couple of years ago — the skill of designing and refining prompts for large language models has largely been absorbed into the broader generative AI engineer role rather than remaining a distinct specialization. Where dedicated prompt engineering roles still exist, they typically combine prompt design with evaluation frameworks for measuring output quality at scale, rather than prompt writing in isolation.

Machine learning engineers typically own the traditional ML lifecycle — feature engineering, model training and evaluation, and deployment of predictive or classification models into production, often outside the context of large language models entirely. This role draws heavily on statistics, data engineering, and MLOps skills, and candidates strong in this area don't always have deep generative AI experience, nor should that be assumed.

'AI Engineer' has become the most commonly used umbrella title, but it still requires specificity in the job description — is this a generative AI application engineer building on top of LLM APIs, an agentic AI engineer building systems that take autonomous multi-step actions, or someone expected to cover the full spectrum from classical ML to generative AI? Leaving this undefined is one of the most common reasons AI hiring processes attract a mismatched, overly broad candidate pool.

Because these titles are used so inconsistently across companies, resume titles alone are a weak signal. A better approach is to ask candidates directly, early in the process, what they actually built and owned day-to-day — training and evaluating models, building retrieval-augmented generation pipelines, or designing multi-step agent workflows — regardless of what their previous employer called the role.

The most effective fix for this ambiguity is avoiding a borrowed title altogether and instead describing the actual outcomes the role needs to deliver in its first six months — for example, 'own the retrieval and prompt pipeline for our customer support assistant' rather than 'AI Engineer needed.' This naturally attracts candidates with the right specific experience and discourages a flood of applicants whose experience doesn't actually match the need.