Machine Learning vs AI Engineer: What Are the Differences?

confusing questions in tech right now is:

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 quality roles.

As a practising machine learning engineer, I want to outline the key differences and similarities between the two roles, so you know exactly which path fits you best.

Let’s get into it!

What Is The Difference?

Being honest, the industry is moving so fast that these titles change definition every quarter.

Not to mention that companies now put “AI” in their job description to make the role seem more prestigious, even though you’ll most likely be doing basic prompt engineering.

However, let me explain the difference, as I have seen firsthand and discussed with other respected practitioners in the field.

In a nutshell, an AI engineer is a software engineer who specialises in the use and integration of foundational GenAI models such as Claude, GPT, BERT, and others. They don’t “build” these models, but rather use them to serve a certain purpose.

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

These are mainly more traditional models like gradient boosted 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.

Alright, enough of me being pedantic, let’s explain them in more detail.

AI Engineer

What is it?

As I mentioned, you have to think of an AI engineer as a software engineer that has a speciality in AI, well, GenAI.

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

These foundational models can do many tasks, like writing code, answering questions, and creating images. That’s why they are foundational, as they can do so many things.

OpenAI’s ChatGPT is the most famous foundational model you’re likely familiar with.

AI engineers don’t train these models; they integrate them into traditional software products and workflows using APIs, self-hosting, etc.

For example, they may embed a chatbot on a shopping website to help customers find what they are looking for more quickly, or add a coding assistant in an IDE, like Cursor.

AI engineering is more product focussed, i.e. you want to deploy something quickly and then refine later.

What do they use?

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

• Solid software engineering skills
• Python, SQL and backend languages like Java or GO are useful
• CI/CD
• Git and GitHub
• LLMs and transformers
• RAG
• Prompt engineering
• Foundational models
• Fine tuning
• Model Context Protocol

Machine Learning Engineer

What is it?

A machine learning engineer focuses on building machine learning models and deploying them into production systems. It originally came from software engineering, but is now its own job.

The significant distinction between machine learning engineers and AI engineers is that the former builds algorithms from scratch that focus on more specific tasks. 

For example, machine learning engineers would build targeted recommendation systems, credit card fraud models and stock forecasting algorithms. These are not “foundational” and have a much narrower use case.

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

Machine learning engineering is more model-focused: you create the model from scratch using available data, test it offline, and ship it when you are happy with its performance.

There also exist further specialties within the machine learning engineer role, like:

• ML platform engineer
• ML hardware engineer
• ML solutions architect

Don’t worry about these if you are a beginner, as they are pretty niche and only relevant after a few years of experience in the field. I just wanted to add these so you know the various options out there.

What do they use?

The tech stack for machine learning engineers is similar to that of AI engineers, with greater emphasis on mathematical abilities.

• Python and SQL, however, some companies may require other languages. For example, in my current role, Rust is needed.
• Git and GitHub
• Bash and Zsh
• AWS, Azure or GCP
• Software engineering fundamentals such as CI/CD, MLOps, and Docker.
• Excellent machine learning knowledge, ideally with a specialism in an area like forecasting, recommendation system or computer vision.
• Solid mathematical understanding of statistics, linear algebra and calculus.

Which One?

As you can see the overlap between skills and work is fairly similar, particularly the foundational software engineering skills.

The main difference lies in the domain specific GenAI knowledge of AI engineers and the deeper mathematical and traditional machine learning knowledge of machine learning engineers.

So, the question stands.

Which one should you pick?

Let’s break down some more logistical features to help you in your decision.

Background

The background for both jobs is similar, typically requiring a master’s in a STEM subject and a couple of years of experience as either a software engineer or a data scientist.

AI engineering is slightly easier to get into, as learning to work with foundational models is a quicker learning curve than understanding all the mathematics behind machine learning.

Demand

Machine learning engineering is the more established role, but that’s mainly because foundational models haven’t existed for long, so the AI engineer role wasn’t required.

However, as AI is now super popular, demand for AI engineers is skyrocketing. You do need to be careful, though, because job titles in this industry are vague, and you need to really read the job description to understand the job you will be doing.

For example, at my company, we technically have AI engineers, but they are still named machine learning engineers. So, titles are kind of erroneous.

Pay

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

Plus, as I just stated, many machine learning engineers are doing AI engineering work, so the salaries are hazy.

Final Choice?

In my opinion, go with what you think you will prefer!

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

If you don’t like research that much and want to quickly ship products using the latest AI tools, then AI engineering is for you!

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

However, suppose you feel a stronger pull towards a career as a machine learning engineer.

In that case, I recommend checking out my last article, where I go step-by-step through how I’d become a successful machine learning engineer all over again.

See you there!