Machine Learning vs AI Engineer: What Are the Differences?

complicated questions in tech proper now could be:

What’s the distinction between an AI engineer and a machine studying engineer?

Each are six-figure jobs, however in the event you select the unsuitable one, you might waste months of your profession studying the unsuitable expertise and miss out on high quality roles.

As a practising machine studying engineer, I wish to define the important thing variations and similarities between the 2 roles, so you already know precisely which path matches you finest.

Let’s get into it!

What Is The Distinction?

Being trustworthy, the business is shifting so quick that these titles change definition each quarter.

To not point out that corporations now put “AI” of their job description to make the function appear extra prestigious, regardless that you’ll more than likely be doing fundamental immediate engineering.

Nonetheless, let me clarify the distinction, as I’ve seen firsthand and mentioned with different revered practitioners within the discipline.

In a nutshell, an AI engineer is a software program engineer who specialises within the use and integration of foundational GenAI models comparable to Claude, GPT, BERT, and others. They don’t “construct” these fashions, however reasonably use them to serve a sure objective.

Alternatively, a machine studying engineer is somebody who truly develops fashions from scratch or utilizing fundamental libraries and builds full end-to-end programs round them.

These are primarily extra conventional fashions like gradient boosted trees and neural networks, however they will also be GenAI fashions.

What I discover humorous about this naming conference, is that machine studying is definitely a subset of AI.

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

Alright, sufficient of me being pedantic, let’s clarify them in additional element.

AI Engineer

What’s it?

As I discussed, you need to consider an AI engineer as a software program engineer that has a speciality in AI, nicely, GenAI.

They primarily work with one thing referred to as foundational fashions, that are large neural networks skilled on oceans of knowledge comparable to textual content, photographs, movies, and audio.

These foundational fashions can do many duties, like writing code, answering questions, and creating photographs. That’s why they’re foundational, as they will accomplish that many issues.

OpenAI’s ChatGPT is essentially the most well-known foundational mannequin you’re seemingly aware of.

AI engineers don’t practice these fashions; they combine them into conventional software program merchandise and workflows utilizing APIs, self-hosting, and so forth.

For instance, they could embed a chatbot on a procuring web site to assist clients discover what they’re searching for extra shortly, or add a coding assistant in an IDE, like Cursor.

AI engineering is extra product focussed, i.e. you wish to deploy one thing shortly after which refine later.

What do they use?

This function is evolving fairly a bit, however normally, you want good information of all the newest GenAI, LLM, and foundational mannequin developments:

• Stable software program engineering expertise
• Python, SQL and backend languages like Java or GO are helpful
• CI/CD
• Git and GitHub
• LLMs and transformers
• RAG
• Immediate engineering
• Foundational fashions
• Fantastic tuning
• Mannequin Context Protocol

Machine Studying Engineer

What’s it?

A machine studying engineer focuses on constructing machine studying fashions and deploying them into manufacturing programs. It initially got here from software program engineering, however is now its personal job.

The numerous distinction between machine studying engineers and AI engineers is that the previous builds algorithms from scratch that target extra particular duties. 

For instance, machine studying engineers would construct focused advice programs, bank card fraud fashions and inventory forecasting algorithms. These aren’t “foundational” and have a a lot narrower use case.

For machine studying engineering, it’s essential know these algorithms at a sophisticated stage, which requires sturdy maths expertise in statistics, linear algebra, and calculus. This isn’t essentially true for an AI engineer.

Machine studying engineering is extra model-focused: you create the mannequin from scratch utilizing out there knowledge, check it offline, and ship it if you end up pleased with its efficiency.

There additionally exist additional specialties inside the machine studying engineer function, like:

• ML platform engineer
• ML {hardware} engineer
• ML options architect

Don’t fear about these in case you are a newbie, as they’re fairly area of interest and solely related after just a few years of expertise within the discipline. I simply wished so as to add these so you already know the varied choices on the market.

What do they use?

The tech stack for machine studying engineers is just like that of AI engineers, with higher emphasis on mathematical skills.

• Python and SQL, nonetheless, some corporations could require different languages. For instance, in my present function, Rust is required.
• Git and GitHub
• Bash and Zsh
• AWS, Azure or GCP
• Software program engineering fundamentals comparable to CI/CD, MLOps, and Docker.
• Wonderful machine studying information, ideally with a specialism in an space like forecasting, advice system or laptop imaginative and prescient.
• Stable mathematical understanding of statistics, linear algebra and calculus.

Which One?

As you’ll be able to see the overlap between expertise and work is pretty comparable, significantly the foundational software program engineering expertise.

The primary distinction lies within the area particular GenAI information of AI engineers and the deeper mathematical and conventional machine studying information of machine studying engineers.

So, the query stands.

Which one do you have to decide?

Let’s break down some extra logistical options that can assist you in your determination.

Background

The background for each jobs is comparable, usually requiring a grasp’s in a STEM topic and a few years of expertise as both a software program engineer or an information scientist.

AI engineering is barely simpler to get into, as studying to work with foundational fashions is a faster studying curve than understanding all of the arithmetic behind machine studying.

Demand

Machine studying engineering is the extra established function, however that’s primarily as a result of foundational fashions haven’t existed for lengthy, so the AI engineer function wasn’t required.

Nonetheless, as AI is now tremendous well-liked, demand for AI engineers is skyrocketing. You do must be cautious, although, as a result of job titles on this business are obscure, and it’s essential actually learn the job description to grasp the job you can be doing.

For instance, at my firm, we technically have AI engineers, however they’re nonetheless named machine studying engineers. So, titles are form of faulty.

Pay

In line with Ranges.fyi, the median wage for a machine learning engineer is £105k (UK) and for an AI engineer is £75k (UK), however I believe this may develop sooner or later.

Plus, as I simply acknowledged, many machine studying engineers are doing AI engineering work, so the salaries are hazy.

Last Alternative?

In my view, go together with what you assume you’ll choose!

If you happen to love maths and understanding how algorithms work underneath the hood, then machine studying engineering is your finest wager.

If you happen to don’t like analysis that a lot and wish to shortly ship merchandise utilizing the newest AI instruments, then AI engineering is for you!

Both method, each roles pay nicely and have glorious long-term profession prospects.

Nonetheless, suppose you’re feeling a stronger pull in direction of a profession as a machine studying engineer.

In that case, I like to recommend trying out my final article, the place I’m going step-by-step by means of how I’d grow to be a profitable machine studying engineer another time.

See you there!