How do an AI Engineer and a Software Engineer differ?

An AI Engineer and a Software Engineer both build software with a coding agent, but a Software Engineer builds systems that give the same output every time, while an AI Engineer builds products on top of AI models whose output can change every time.

In this blog, we will learn about how an AI Engineer and a Software Engineer differ. We will also see what a Software Engineer does, what an AI Engineer does, why a model makes the work different, what extra work the AI Engineer takes on, how the two roles compare side by side, and which role to pick based on our situation.

We will cover the following:

What is a Software Engineer?
What is an AI Engineer?
The core difference: deterministic vs probabilistic
What the AI Engineer adds on top
Comparison between an AI Engineer and a Software Engineer
Which role should we pick?

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 a Software Engineer?

A Software Engineer is a person who designs, builds, tests, and maintains software systems.

The software can be a mobile app, a website, or a backend service. The Software Engineer takes a problem, breaks it into small parts, and builds each part with a coding agent such as Claude Code. The coding agent writes the code, and the engineer reviews it, tests it, and decides whether it is correct.

Let's say we are building a food delivery app. The Software Engineer builds the login flow, the cart, the payment step, the order tracking screen, and the database behind all of them.

The key property of this work is that the system is deterministic. Means, if we give the same input, we get the same output, every time. If the cart has two items of 100 rupees each, the total is 200.

So, the Software Engineer can write a test like below:

def test_cart_total():
    cart = Cart()
    cart.add(item_price=100)
    cart.add(item_price=100)
    assert cart.total() == 200

Here, we are adding two items and checking that the total is exactly 200. If the test passes once, it will pass every time, unless someone changes the code.

What is an AI Engineer?

An AI Engineer is a person who builds products on top of existing AI models, such as an LLM (Large Language Model), by connecting the model to data, tools, and users.

The AI Engineer does not usually train a model from scratch. The model is available through an API. The AI Engineer's work is to make that model useful inside a product: a support assistant, a document search tool, or an agent that completes a task on its own.

In the same food delivery app, the AI Engineer builds the support assistant. A user types "my order is late and the food is cold", and the assistant must understand the complaint, look up the order, decide whether a refund is allowed, and reply politely.

The AI Engineer also builds this with a coding agent. The code for calling the model, fetching the order, and sending the reply is written by the coding agent and reviewed by the engineer. This building part is the same for both roles.

The difference is in what the code is wrapped around. In the middle of this system sits a model, and the model is probabilistic. Means, if we give the same input, we can get a different output each time.

So, a test like below does not work:

def test_support_reply():
    reply = assistant.reply("my order is late and the food is cold")
    assert reply == "Sorry for the delay. We have issued a refund."

Here, the test checks for one exact sentence. The model can reply with the same meaning in different words, and the test fails even when the reply is correct. And, the model can reply with a wrong refund decision in a polite sentence, and no test catches it.

The AI Engineer must find another way to know if the system is working. Here, the extra work of the AI Engineer comes into the picture.

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.

The core difference: deterministic vs probabilistic

Both roles build software with a coding agent, but one builds a deterministic system and the other builds a system around a probabilistic model. Let's see what this changes in practice.

Correctness: For the Software Engineer, the code is either right or wrong, and a test proves it. For the AI Engineer, the output is right most of the time, and the job is to measure how often and push that number up.

Debugging: When the cart total is wrong, the Software Engineer reads the code and finds the exact line that is wrong. When the assistant gives a bad reply, there is no line to point to. The AI Engineer must check the prompt, the fetched data, the chosen model, and the input to find out why.

Failure: A deterministic system fails with an error, and we see it. A model fails without any error. It replies with full confidence and a wrong fact, and nobody notices unless we check.

Change: The Software Engineer's code does the same thing until someone changes it. The AI Engineer's system can change its behavior when the model provider updates the model, even if our code did not change at all.

This is why the same food delivery app needs two different ways of thinking. The cart needs a test, and the assistant needs an evaluation.

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.

What the AI Engineer adds on top

The AI Engineer still needs everything a Software Engineer knows, and adds five more areas on top of it.

Evaluation: Since one test cannot prove correctness, the AI Engineer builds a set of example inputs with expected outputs, runs the system on all of them, and scores the results. For the support assistant, this can be 200 real complaints with the correct action for each one. If the system gets 180 of them right, the score is 90 percent. Every change to the prompt or the model is checked against this score before it ships.

Prompts: The prompt is the instruction given to the model. The AI Engineer writes it, tests it against the evaluation set, and keeps it in version control like code. A small wording change in the prompt can move the score up or down.

Retrieval: The model does not know our order data. The AI Engineer builds a step that fetches the right information, such as the order details and the refund policy, and puts it into the prompt before the model answers. This is called RAG (Retrieval Augmented Generation). The quality of the answer depends on the quality of what is fetched.

Agents: Sometimes the model must take actions, not just answer. The AI Engineer builds an agent, which is a system built around an LLM that lets the model recommend a tool and its input, such as "look up order 4521", and then our code runs that tool and gives the result back to the model. The AI Engineer decides which tools exist, what each tool can do, and when the loop must stop.

Cost and latency of model calls: A normal function call costs almost nothing and returns in milliseconds. A model call costs money for every token sent and received, and it can take a few seconds. The AI Engineer must count tokens, cache repeated work, choose a smaller model where a big one is not needed, and keep the reply time acceptable for the user.

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Comparison between an AI Engineer and a Software Engineer

Let me tabulate the differences between an AI Engineer and a Software Engineer for your better understanding.

Aspect	Software Engineer	AI Engineer
What they build	Apps, services, and tools	Products on top of AI models
How they build	With a coding agent, then review and test	With a coding agent, then review, test, and evaluate
Nature of the system	Deterministic, same output for same input	Probabilistic, output can change for same input
How correctness is checked	Unit tests and integration tests	Evaluation sets with scores, plus the usual tests
How failure shows up	Errors and crashes	Confident wrong answers
Debugging	Find the wrong line of code	Check the prompt, the retrieved data, the model, the input
Extra areas	None beyond standard engineering	Evaluation, prompts, retrieval, agents, cost and latency
Cost of one call	Close to zero	Paid per token, must be managed
Speed of one call	Milliseconds	Seconds, must be managed
Core skill	Agentic coding plus system design	Agentic coding plus judgment about model output

Which role should we pick?

We must choose based on our situation and what we enjoy.

If we like problems with one correct answer, where a passing test means the work is done, the Software Engineer role fits well.

If we like problems where the answer is "mostly right" and the job is to measure it and improve it, the AI Engineer role fits well. We must be comfortable with a system that we cannot fully control.

If we are already a Software Engineer, moving to AI Engineer is a short path. Our agentic coding and system design carry over completely. The gap is the five extra areas: evaluation, prompts, retrieval, agents, and cost and latency of model calls. We can fill that gap by building one product on top of an LLM with a coding agent, writing an evaluation set for it, and improving the score.

If we are starting fresh, we must learn the Software Engineer foundation first. An AI Engineer who cannot design a system, read the code the coding agent produces, or verify that it works, cannot ship a reliable AI product either.

Now we must have understood how an AI Engineer and a Software Engineer differ, why the model changes the work, and which role fits our situation.

Frequently Asked Questions

Does an AI Engineer train models from scratch?

No. An AI Engineer does not usually train a model from scratch. The model is available through an API. The work is to make that model useful inside a product, such as a support assistant, a document search tool, or an agent that completes a task on its own.

Why does a normal unit test not work for an AI system?

A normal unit test checks for one exact output, but a model can give a different output for the same input. The model can reply with the same meaning in different words, and the test fails even when the reply is correct. So, the AI Engineer builds an evaluation set, runs the system on all of it, and scores the results.

Can an AI system change its behavior even if our code does not change?

Yes. An AI system can change its behavior when the model provider updates the model, even if our code did not change at all. A Software Engineer's code, on the other hand, does the same thing until someone changes it.

Do both an AI Engineer and a Software Engineer use a coding agent?

Yes. Both roles build software with a coding agent such as Claude Code. The coding agent writes the code, and the engineer reviews it, tests it, and decides whether it is correct. The difference is that the AI Engineer wraps that code around a probabilistic model and must also evaluate its output.

That's it for now.

Thanks

Amit Shekhar
Founder @ Outcome School

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