How do a Generative AI Engineer and an AI Engineer differ?

A Generative AI Engineer and an AI Engineer do the same job in practice, which is building products on top of existing AI models. The Generative AI Engineer title stresses models that generate text, image, audio, and video, while the AI Engineer title is the broader umbrella that also covers classification, recommendation, and vision features built on existing models.

In this blog, we will learn about how a Generative AI Engineer and an AI Engineer differ. We will also see what an AI Engineer does, what a Generative AI Engineer does, why two titles exist for one job, how to read a job description that uses either title, how the two compare side by side, and which title to look for based on our situation.

We will cover the following:

• What is an AI Engineer?
• What is a Generative AI Engineer?
• Why do two titles exist for one job?
• How to read a job description with either title
• Difference between Generative AI Engineer and AI Engineer
• Which title should we look for?

I am Amit Shekhar, Founder @ Outcome School, I have taught and mentored many developers, and their efforts landed them high-paying tech jobs, helped many tech companies in solving their unique problems, and created many open-source libraries being used by top companies. I am passionate about sharing knowledge through open-source, blogs, and videos.

I teach AI and Machine Learning at Outcome School.

Let's get started.

What is an AI Engineer?

An AI Engineer is a software engineer who builds products on top of existing AI models.

The important word here is existing. Model providers have already trained large models. An AI Engineer takes one of those models and builds a product around it, and does not usually train a model from scratch.

Let's say a company wants a support assistant that answers customer questions using the company's own help documents. The model already knows how to read and write. But, it does not know the company's documents. So, the AI Engineer builds the system that fetches the right documents, passes them to the model, and returns the answer to the user.

This is what an AI Engineer owns:

• Calling the model through an API and designing the prompts.
• Building RAG, which connects the model to the company's own data.
• Building agents, where the model picks a tool like search or a database and our code runs it.
• Writing evaluations to measure whether the output is correct.
• Deploying the system and keeping it fast, cheap, and reliable.

Now, an AI Engineer does not work only with text models. An AI Engineer also builds features like these on top of existing models:

• A classification feature that sorts incoming emails into billing, sales, and support.
• A recommendation feature that suggests the next product to a shopper.
• A vision feature that reads a photo of a receipt and extracts the total.

Here, the model is still an existing one, but the output is a label, a ranked list, or a few extracted fields, and not a paragraph of generated text. The AI Engineer title covers all of this.

An AI Engineer builds all of it with a coding agent. We describe the task to the coding agent, it writes the connector or the pipeline, and we review it, test it, and design the system around it. The judgment and the verification are our job.

This was about the AI Engineer. Now, it's time to learn about the Generative AI Engineer.

What is a Generative AI Engineer?

A Generative AI Engineer is an AI Engineer whose work is focused on generative models.

A generative model is a model that creates new content. It generates text, images, audio, or video. An LLM (Large Language Model) is a generative model for text, and an image model is a generative model for pictures.

Let's take the same support assistant example. A Generative AI Engineer builds exactly the same system, where the model reads the documents and generates an answer. The prompts, the RAG pipeline, the evaluation, and the deployment are the same work.

This is what a Generative AI Engineer owns:

• Calling generative models through an API and designing the prompts.
• Building RAG so that the generated answer is grounded in real data.
• Building agents around an LLM.
• Writing evaluations for generated output, which is harder because there is no single correct answer.
• Deploying the system and controlling latency and cost.

The only place where the work differs is the type of output. A Generative AI Engineer is more likely to work on features like these:

• A writing assistant that drafts an email from a few bullet points.
• An image feature that generates a product photo from a text description.
• A voice feature that turns an article into spoken audio.

Here, the output is new content, and its quality is what we measure.

A Generative AI Engineer also builds with a coding agent, and then reviews and verifies what the coding agent produces.

Now, if we compare the two lists of responsibilities above, we can notice that they are almost the same. In practice, the two titles describe the same job.

A quick note for you

No matter which tech domain you work in, get familiar with these topics:

• LLM
• RAG
• MCP
• Agent
• Fine-tuning
• Quantization

We put it all together in one video:

AI Engineering Explained: LLM, RAG, MCP, Agent, Fine-Tuning, and Quantization

No need to stop reading - bookmark it and watch later when you get time. Future you will thank you.

Now, let's get back to the topic.

Why do two titles exist for one job?

The two titles exist because of naming, not because of different work.

Traditionally, most AI work was called Machine Learning Engineering, and it involved training models on company data. The models made predictions, like a label or a number, and not content.

Then LLMs and image models arrived, and the most visible AI products started generating text and images. Many companies named the job of building on top of these models Generative AI Engineer, because the word generative described what was new.

At the same time, other companies named the same job AI Engineer. Their reasoning was that building on top of existing models is the job, and whether the model is generative or not is only a detail.

So, the two titles grew side by side for the same work.

Means, when we see either title, we must not assume a different set of skills. We must read the job description.

Want to become a Generative AI Engineer?

These are the skills this role needs, and we teach them in our AI and Machine Learning Program at Outcome School:

• LLM Fundamentals
• Prompt Engineering
• RAG
• Fine-tuning
• Multimodal AI
• Diffusion Models

We take you from the fundamentals to building real AI systems, step by step.

How to read a job description with either title

The title tells us very less. The responsibilities section tells us the actual work.

Step 1: Look at the models named in the description. If it names LLMs, image models, or speech models, the work is generative. If it also names classification, ranking, or vision models, the role is broader, and that is more common under the AI Engineer title.

Step 2: Look at the responsibilities. The words we expect to see are prompt design, RAG, agents, tool calling, evaluation, deployment, and the ability to build with a coding agent and verify its output. If these words are present, it is the job we described above, under either title.

Step 3: Look for training. If the description says "train models from scratch", "design model architectures", or "run large training jobs", then the title is misleading and the job is closer to a Machine Learning Engineer or a research role.

Let's take two example descriptions.

The first says: "Generative AI Engineer. Build LLM-based features, design prompts, implement RAG over internal documents, build evaluation pipelines, and deploy to production."

The second says: "AI Engineer. Build features on top of foundation models, including LLM-based assistants, document classification, and image understanding. Design prompts, build RAG and agents, write evaluations, and own deployment."

Here, we can see that both ask for the same core skills. The second one adds classification and image understanding, which is the broader scope that the AI Engineer title often carries. A person who can do the first job needs only a small addition to do the second.

Stay updated: Subscribe to our newsletter to get our latest AI and Machine Learning blogs straight to your inbox.

Difference between Generative AI Engineer and AI Engineer

Let me tabulate the differences between Generative AI Engineer and AI Engineer for your better understanding.

Here, we can see that most rows are the same. The only real difference is the scope of models, and even that is small, because most of the work in both roles is with LLMs.

Which title should we look for?

We must choose based on our situation.

If we are starting out and want the widest set of opportunities, we must search for both titles. Searching for only one of them hides many openings for the same job.

If our interest is in text, image, audio, and video generation, both titles will give us that work, and the Generative AI Engineer title makes it explicit.

If we also enjoy building classification, recommendation, and vision features on top of existing models, the AI Engineer title is the better match, because that broader scope is more common under it.

If we are already working under one title, we do not need to switch titles, because the skills are the same.

And, in every case, we must read the responsibilities section before applying.

Now we must have understood how a Generative AI Engineer and an AI Engineer differ, why both titles exist for the same job, and how to read a job description that uses either title.

Frequently Asked Questions

Does a Generative AI Engineer need different skills from an AI Engineer?

No. A Generative AI Engineer and an AI Engineer need the same core skills: prompt design, RAG, agents, evaluation, and deployment. Both build with a coding agent and verify what it produces. The only real difference is the scope of models, and even that is small, because most of the work in both roles is with LLMs.

Does a Generative AI Engineer train models from scratch?

No. A Generative AI Engineer, like an AI Engineer, builds products on top of existing models and does not train a model from scratch. If a job description asks to train models from scratch or run large training jobs, the title is misleading. That job is closer to a Machine Learning Engineer or a research role.

Is an LLM a generative model?

Yes. An LLM is a generative model for text. A generative model is a model that creates new content, such as text, images, audio, or video. An image model is also a generative model, one that generates pictures.

Why is evaluating generated output harder?

Evaluating generated output is harder because there is no single correct answer. For a classification feature, we can measure the accuracy of the labels. A writing assistant drafts new content, and its quality is what we measure. So, the evaluation must judge the quality of the generated content.

That's it for now.

Thanks

Amit Shekhar
Founder @ Outcome School

You can connect with me on:

• X
• LinkedIn
• GitHub

Follow Outcome School on:

• X
• YouTube
• LinkedIn
• GitHub

Read all of our high-quality blogs here.