What is an AI Engineer?
EU AI Jobs ·
"AI Engineer" is one of the most common job titles in tech right now. It is also one of the most confusing, because people use it for two different jobs. Here is what it means, in plain words.
The short answer
An AI Engineer is a software engineer who builds products that use AI.
They usually don't create the AI model itself. They take a model that already exists, such as the ones behind ChatGPT, Claude or Gemini, and build something useful around it: a support assistant, a tool that reads contracts, a feature that writes product descriptions.
Why the title is confusing
The title has two meanings, and job ads mix them up.
- The older meaning: someone who builds and trains machine learning models. Today, most companies call this a Machine Learning Engineer.
- Today's meaning: someone who builds applications on top of models made by others, usually through an API. This is what most job ads mean when they say "AI Engineer" now.
If you are not sure which one a job means, read the duties, not the title. "Train", "fine-tune" and "build models" point to the older meaning. "Integrate", "build with LLMs" and "ship features" point to today's.
A simple example
Imagine an online shop that gets thousands of customer questions a day. Most are the same: where is my order, how do I return this, does it come in blue?
The shop hires an AI Engineer, who:
- connects a language model to the shop's help pages and order system;
- makes sure the assistant answers only from the shop's own information, not from guesses;
- writes tests that check the answers are right, before anything goes live;
- watches what it costs, since every answer from the model has a price;
- hands a question to a person when the assistant is not sure.
A few weeks later, most simple questions get an answer in seconds, and the support team spends its time on the hard ones.
How is it different from other jobs?
| Role | What they build | Main question they answer |
|---|---|---|
| Software engineer | Apps and systems | Does the product work? |
| AI Engineer | Products on top of existing AI models | Does the AI give users what they need? |
| Machine Learning Engineer | The models themselves | Is the model accurate, fast and cheap? |
| Data scientist | Analyses and predictions from data | What does the data tell the business? |
Words you will see in AI Engineer job ads
- LLM (large language model): the kind of AI behind ChatGPT, Claude and Gemini. It reads and writes text.
- API: the way a program talks to a model over the internet, without running the model itself.
- Prompt: the instructions given to a model.
- RAG (retrieval-augmented generation): finding the right documents first, then giving them to the model, so it answers from real information.
- Agent: an AI system that can take steps on its own, such as searching, calling tools or filling in a form.
- Evals: tests that measure whether the AI's answers are good enough.
- Fine-tuning: training an existing model a little more on your own examples.
What skills do you need?
Technical
- Solid software engineering, usually in Python or TypeScript.
- Working with model APIs, prompts, RAG and agents.
- Testing AI output, since the same question can get a different answer each time.
Product and people
- Turning a vague business problem into something an AI can do well.
- Explaining to non-technical colleagues what the AI can and can't do.
Is it right for you?
It may suit you if you are a software engineer who likes building things people use, and you enjoy a field that changes every few months.
It may not suit you if you want to work on the maths behind AI. In that case, look at Machine Learning Engineer or research roles instead.
Frequently asked questions
Is an AI Engineer the same as a Machine Learning Engineer?
Not usually. An AI Engineer builds products on top of existing models; a Machine Learning Engineer builds and trains the models. Some companies still use one title for the other, so read the duties in the job ad.
Can a software engineer become an AI Engineer?
Yes. The job is software engineering plus working with model APIs, prompts, RAG and testing AI output, so it is a natural next step for software engineers.
Do AI Engineers need advanced maths?
Less than Machine Learning Engineers do. Solid software engineering matters most, along with knowing how language models behave and where they fail.
What does RAG mean?
Retrieval-augmented generation: finding the right documents first, then giving them to the model, so it answers from real information.
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