Machine Learning vs AI Engineering: What's the Difference?

The most confusing questions in tech right now:

What is the difference between an AI engineer and a machine learning engineer?

Both are six-figure jobs, but if you choose the wrong one, you could waste months of your career learning the wrong skills and miss out on high-quality roles.

As a practicing machine learning engineer, I want to outline the main differences and similarities between the two roles, so you know exactly which approach suits you best.

Let's get into it!

What's the Difference?

To be honest, the industry moves so fast that these topics change meaning every quarter.

Not to mention that companies are now putting “AI” in their job description to make the role seem more prestigious, even though you will most likely be doing rapid engineering.

However, let me explain the difference, as I have seen it myself and talked to other respected doctors in the field.

In a nutshell, an AI engineer is a software engineer who specializes in the implementation and integration of basic GenAI models such as Claude, GPT, BERT, and others. They do not “build” these models, but rather use them to achieve a specific purpose.

On the other hand, a machine learning engineer is someone who develops models from scratch or uses basic libraries and builds complete end-to-end systems around them.

These are mainly traditional models like gradient enhanced trees and neural networks, but they can also be GenAI models.

What I find funny about this naming convention, is that machine learning is actually a subset of AI.

So an AI engineer is technically a GenAI engineer, if anything.

Okay, enough of me being pedantic, let's explain ourselves in more detail.

An AI engineer

What's going on?

As I said, you should think of an AI engineer as a software engineer specializing in AI, well, GenAI.

They mainly work with something called base models, which are large neural networks trained on seas of data such as text, images, videos, and audio.

These basic models can perform many tasks, such as writing code, answering questions, and creating images. That is why they are basic, as they can do many things.

OpenAI's ChatGPT is a very popular basic model that you may be familiar with.

AI developers don't train these models; integrate them into traditional software products and workflows using APIs, automation, etc.

For example, they might embed a chatbot on a shopping website to help customers find what they're looking for quickly, or add a coding assistant to an IDE, such as Cursor.

AI engineering is very product oriented, meaning you want to ship something quickly and refine it later.

What do they use?

This role is evolving a bit, but in general, you need a good knowledge of all GenAI, the latest LLM, and basic modeling trends:

• Strong software engineering skills
• Python, SQL and backend languages ​​like Java or GO are helpful
• CI/CD
• Git and GitHub
• LLMs and transformers
• RAG
• Fast engineering
• Basic models
• Fine tuning
• Model Content Protocol

Machine Learning Engineer

What's going on?

A machine learning engineer specializes in building machine learning models and applying them to manufacturing applications. It originally came from software engineering, but now it's a profession of its own.

The key difference between machine learning engineers and AI engineers is that the former build algorithms from scratch that focus on specific tasks.

For example, machine learning engineers will build targeted recommendation systems, credit card fraud models and stock forecasting algorithms. These are not “basic” and have very little use.

For machine learning engineering, you need to know these algorithms at an advanced level, which requires strong mathematical skills in mathematics, linear algebra, and calculus. This is not necessarily true for an AI engineer.

Machine learning engineering is very model-oriented: you create a model from scratch using available data, test it offline, and deploy it when you're happy with its performance.

There are also other specialties within the machine learning engineer role, such as:

• ML platform engineer
• ML hardware developer
• Builder of ML solutions

Don't worry about this if you are just starting out, as they are good and only worth it after a few years of experience in the field. I wanted to add these so you know the different options out there.

What do they use?

The skill stack of machine learning engineers is similar to that of AI engineers, with a strong emphasis on statistical skills.

• Python and SQL, however, some companies may require other languages. For example, in my current role, Rust it is necessary.
• Git and GitHub
• Bash and Zsh
• AWS, Azure or GCP
• Fundamentals of software engineering such as CI/CDMLOps, as well Docker.
• Excellent knowledge of machine learning, suitable for specialization in an area such as prediction, recommendation system or computer vision.
• Strong mathematical understanding of calculus, linear algebra and calculus.

Where is it?

As you can see the overlap between skills and work is similar, especially the basic skills of software engineering.

The main difference lies in the specific areas of GenAI knowledge for AI engineers and deep mathematical and traditional machine learning knowledge for machine learning engineers.

So, the question stands.

Which should you choose?

Let's break down some planning features to help you in your decision.

It's the background

The background for both jobs is similar, usually requiring a master's in a STEM subject and a few years of experience as a software engineer or data scientist.

AI engineering is easy to get into, as learning to work with basic models is a faster learning curve than understanding all the math behind machine learning.

Need

Machine learning engineering is a more established role, but that's because the underlying models haven't been around for a long time, so the AI ​​engineering role hasn't been needed.

However, as AI is now becoming more popular, the demand for AI engineers is increasing. You need to be careful, however, because the job titles in this industry are not clear, and you need to read the job description to understand the work you will be doing.

For example, at my company, technically we have AI engineers, but they are still called machine learning engineers. Therefore, the articles are wrong.

Pay up

According to Levels.fyi, the average salary for a machine learning engineer is £105k (UK) and for an AI engineer is £75k (UK), but I think this will increase in the future.

Also, as I just said, many machine learning engineers do AI engineering work, so the salaries are not good.

The Final Choice?

In my opinion, go with what you think you'll like!

If you love math and understand how algorithms work under the hood, then machine learning engineering is your best bet.

If you don't like a lot of research and want to quickly ship products using the latest AI tools, AI engineering is for you!

Either way, both roles pay well and have excellent long-term career prospects.

However, let's say you feel strongly drawn to a career as a machine learning engineer.

If so, I recommend that you check out my last article, where I go step by step on how to become a successful machine learning developer again.

See you there!