What is an AI Engineer and what do they actually do?

An AI Engineer is a software engineer who builds products such as a chat assistant, a document search tool, or an AI agent on top of existing AI models, instead of training new models from scratch.

In this blog, we will learn about what an AI Engineer is and what they actually do. We will also see what an AI Engineer owns in a product, how they build with a coding agent, how a normal day in the role looks, how the role differs from a Machine Learning Engineer, and who this role suits.

We will cover the following:

What is an AI Engineer?
What does an AI Engineer own?
How does an AI Engineer build?
A day in the life of an AI Engineer
AI Engineer vs Machine Learning Engineer
Who should become an AI Engineer?

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 an engineer who takes an existing AI model, such as an LLM (Large Language Model), and builds a working product around it.

The model already exists. AI labs train these models and make them available through an API. The AI Engineer does not train the model. The AI Engineer uses the model.

Let's say a company wants a support assistant that answers customer questions about its own products. The LLM knows how to read and write text, but it knows nothing about this company's products, its refund policy, or its order history. Someone has to connect the model to the company's data, decide what the model can and cannot say, test the answers, and put the whole thing on a server where customers can use it. That someone is the AI Engineer.

One more thing to notice. Some job posts say Generative AI Engineer instead of AI Engineer. In practice, it is the same role. The word "generative" only stresses that the models generate text, images, or audio. We must treat it as a naming difference, not a different job.

What does an AI Engineer own?

An AI Engineer owns everything between the model and the user. Let's see each part.

Model APIs: The AI Engineer connects the product to a model through an API, writes the prompts, and handles the model's response and its failures, such as timeouts and request limits. They also decide which model to use for which task, because a small, fast model is enough for simple tasks and a bigger model is needed for hard ones.

RAG: RAG stands for Retrieval Augmented Generation. The model does not know the company's private data, so the AI Engineer builds a pipeline that searches the company's documents, picks the relevant pieces, and gives them to the model along with the question. This is how the support assistant in our example answers questions about the refund policy. Each document is split into small pieces, and each piece is turned into a list of numbers called an embedding, so that pieces with a similar meaning can be found quickly. The AI Engineer owns how the documents are split, how the embeddings are stored, how they are searched, and how much text is given to the model.

Agents: An AI Agent is a system built around an LLM that can take actions, such as looking up an order, sending an email, or creating a ticket. The LLM recommends which tool to use and with what input, and the agent code runs it. The AI Engineer owns the tools, the loop that runs the agent, the memory, and the rules about what the agent can and cannot do.

Evaluation: A model can give a different answer to the same question, and a small change in a prompt can change many answers at once. So, the AI Engineer builds a test set of real questions with expected answers, runs every question through the product, and measures how often the answer is correct. Every change to a prompt, a model, or a retrieval step is checked against this test set before it goes live. Without evaluation, the team is only guessing whether a change made things better or worse.

Deployment: The product has to run on a server, handle many users at the same time, stay within a cost budget, log every request, and have a way to monitor failures. The AI Engineer owns this part too.

So, an AI Engineer is a full-stack engineer for AI products, where the stack is the model, the data, the agent, the evaluation, and the deployment.

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.

How does an AI Engineer build?

Now, let's understand how the building happens.

An AI Engineer builds with an AI coding agent, such as Claude Code or Codex. The engineer does not sit and type every line. The engineer describes the task clearly to the coding agent, the coding agent writes the code, and the engineer reviews it, tests it, and fixes the design where it is wrong.

Let's take our support assistant example. We need a connector that pulls the company's help articles into our search index. We direct the coding agent as below:

Write a connector that reads all help articles from our help center API,
splits each article into chunks of about 500 tokens,
creates an embedding for each chunk,
and stores the chunk and its embedding in our search index.
Add retries for API failures and log every skipped article.

The coding agent writes the connector. Now, the work of the AI Engineer begins.

We read the code and check that the chunk size is right for our documents. We run it on ten articles and check that the chunks are stored correctly. We send a few test questions and check that the right chunks come back. We check what happens when the help center API is down.

Here, we can notice that the engineer's job is not typing. The engineer's job is describing the task clearly, reviewing and verifying what the coding agent produces, testing it, and designing the system around it. The coding agent produces the code, and the engineer provides the judgment.

This is why the key skill of an AI Engineer is strong agentic coding, which means the ability to build with a coding agent and verify its output.

Want to become an AI Engineer?

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

RAG
Vector Databases
AI Agent
Tool use in Agents
Evaluation of LLMs and Agents
Model Deployment and Serving

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

A day in the life of an AI Engineer

The best way to understand the role is to walk through one working day.

Morning: The day starts with the evaluation dashboard. Last night, the team switched the support assistant to a newer model. The evaluation score on the refund questions dropped from 92 percent to 85 percent. So, the first task is to find out why.

We pull the failed cases and read them. The new model is answering in a longer format, and it is skipping the exact number of days allowed for a refund in some answers. We adjust the prompt to state the refund period first, ask the coding agent to update the prompt template and the test, re-run the evaluation, and the score comes back to 93 percent.

Midday: A product manager wants the assistant to also create a return request, not just explain the policy. This is a new tool for the agent.

We design the tool first: what input it needs, what it returns, and what it must never do, such as creating a return without a confirmed order number. Then we direct the coding agent to write the tool and the tests. We review the code, run it against a fake order service, and add the tool to the agent. We also add five new evaluation cases for return requests.

Afternoon: A cost report shows the monthly API spend went up. We check the logs and find that some requests are sending the full conversation history, including old messages that are no longer needed. We trim the history to the last few turns plus a short summary, verify the evaluation score stays the same, and release it.

End of day: We write a short note for the team: what changed, what the evaluation score was before and after, and what to watch for. Then we review a pull request from a teammate who is adding a new document source to the RAG pipeline.

Here, we can see that most of the day is spent on reading outputs, measuring, deciding, and verifying. Very less of it is spent on typing code.

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AI Engineer vs Machine Learning Engineer

The nearest role to an AI Engineer is a Machine Learning Engineer. A Machine Learning Engineer works on the model itself: collecting and cleaning training data, choosing the model architecture, training the model, tuning it, and serving it. An AI Engineer works on the product around the model: APIs, RAG, agents, evaluation, and deployment. A Machine Learning Engineer asks "how do we make this model better?", and an AI Engineer asks "how do we build a reliable product with this model?". A Machine Learning Engineer needs deeper mathematics and knowledge of training infrastructure, while an AI Engineer needs strong software engineering, agentic coding, and product judgment. The two roles work together: the Machine Learning Engineer may fine-tune a model, which means training it further on the company's own data, and the AI Engineer puts that model into the product and measures whether it helps.

Who should become an AI Engineer?

This role suits a few kinds of people.

Software engineers who already build backend or full-stack products and want to add AI to them. Most of the AI Engineer's day is software engineering, so this background transfers directly.
People who like building products more than researching models. If the question "does this work for the user?" interests you more than "can we beat the benchmark?", this role fits.
People who are comfortable with uncertainty. The output of a model varies, so an AI Engineer must be patient with measuring, testing, and iterating instead of expecting the code to be right the first time.
People who can direct and verify a coding agent. The role rewards those who can describe a task clearly, read the result critically, and catch what is wrong.

This role does not need a research background or a deep mathematics background. We do not need to know how to train a model to use a model well. We need to know how the model behaves, where it fails, and how to build around those failures.

Now we must have understood what an AI Engineer is, what they own, how they build with a coding agent, how their day looks, how they differ from a Machine Learning Engineer, and who this role suits.

Frequently Asked Questions

Is a Generative AI Engineer a different role from an AI Engineer?

No. In practice, a Generative AI Engineer is the same role as an AI Engineer. The word "generative" only stresses that the models generate text, images, or audio. We must treat it as a naming difference, not a different job.

Does an AI Engineer train the model?

No. AI labs train the models and make them available through an API, and the AI Engineer uses them. When a model needs more training on the company's own data, a Machine Learning Engineer may fine-tune it. The AI Engineer then puts that model into the product and measures whether it helps.

Does an AI Engineer need a research or deep mathematics background?

No. This role does not need a research background or a deep mathematics background. We do not need to know how to train a model to use a model well. We need to know how the model behaves, where it fails, and how to build around those failures.

Why can an AI Engineer not skip evaluation?

Without evaluation, the team is only guessing whether a change made things better or worse. A model can give a different answer to the same question, and a small change in a prompt can change many answers at once. So, every change is checked against a test set of real questions before it goes live.

That's it for now.

Thanks

Amit Shekhar
Founder @ Outcome School

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