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In practice, prompt engineering jobs include writing system prompts and few-shot examples, specifying output formats, building evaluation sets, stress-testing for failures and misuse, documenting what works, and monitoring outputs once they are live. It is collaborative. The person works with product managers who define the goals, engineers who wire prompts into applications, and subject experts who judge quality. The title became famous in 2023, when AI companies and startups posted well-paid prompt engineer listings that were widely covered in the press. Since then three things have changed. Models follow instructions much better, so fewer tricks are needed. Tools for automatic prompt optimization, such as the open-source DSPy framework, can search for better prompts programmatically. And the work has broadened into what many practitioners call context engineering: deciding which documents, tool results, memory and conversation history go into the model's context window, and in what form. As a result, dedicated prompt engineer titles are relatively rare, while the skill appears inside other roles: AI engineer, applied AI specialist, conversation designer, AI content strategist, LLM evaluation specialist, solutions engineer, and domain experts in law, medicine or education who shape AI tools for their field. Two misconceptions are worth correcting. Prompt engineering is not about secret magic phrases. It is systematic experimentation with measured results. It is also not a job that needs no skills. Strong practitioners combine precise writing, domain expertise and at least basic coding in Python and API calls, so they can run tests at scale. Good portfolio projects show a process, not just a clever prompt: a defined task, a test set, baseline results, iterations with reasons, and final metrics. Examples include a document extraction pipeline, a customer support prompt with edge-case tests, or a comparison of how one prompt performs across several models.