Machine Studying vs AI Engineer: What Are the Variations?

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 when you select the incorrect one, you could possibly waste months of your profession studying the incorrect abilities 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 realize precisely which path matches you finest.

Let’s get into it!

What Is The Distinction?

Being sincere, the business is transferring so quick that these titles change definition each quarter.

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

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

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

Then again, a machine studying engineer is somebody who really develops fashions from scratch or utilizing primary libraries and builds full end-to-end methods round them.

These are primarily extra conventional fashions like gradient boosted bushes 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, it’s a must to consider an AI engineer as a software program engineer that has a speciality in AI, effectively, GenAI.

They primarily work with one thing known as foundational fashions, that are big neural networks skilled on oceans of knowledge similar to textual content, photos, movies, and audio.

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

OpenAI’s ChatGPT is probably the most well-known foundational mannequin you’re doubtless conversant in.

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

For instance, they could embed a chatbot on a procuring web site to assist prospects discover what they’re on the lookout 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 abilities
• Python, SQL and backend languages like Java or GO are helpful
• CI/CD
• Git and GitHub
• LLMs and transformers
• RAG
• Immediate engineering
• Foundational fashions
• High quality 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 methods. 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 suggestion methods, bank card fraud fashions and inventory forecasting algorithms. These usually are not “foundational” and have a a lot narrower use case.

For machine studying engineering, it’s essential to know these algorithms at a sophisticated degree, which requires robust maths abilities 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 obtainable knowledge, check it offline, and ship it if you end up proud of its efficiency.

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

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

Don’t fear about these if you’re a newbie, as they’re fairly area of interest and solely related after a couple of years of expertise within the area. I simply needed so as to add these so you realize the assorted choices on the market.

What do they use?

The tech stack for machine studying engineers is much like that of AI engineers, with better emphasis on mathematical talents.

• Python and SQL, nevertheless, some firms might 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 similar to CI/CD, MLOps, and Docker.
• Glorious machine studying information, ideally with a specialism in an space like forecasting, suggestion system or pc imaginative and prescient.
• Stable mathematical understanding of statistics, linear algebra and calculus.

Which One?

As you may see the overlap between abilities and work is pretty comparable, notably the foundational software program engineering abilities.

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 choose?

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

Background

The background for each jobs is comparable, sometimes requiring a grasp’s in a STEM topic and a few years of expertise as both a software program engineer or a knowledge 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 to actually learn the job description to grasp the job you’ll be doing.

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

Pay

In line with Ranges.fyi, the median wage for a machine studying 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 said, many machine studying engineers are doing AI engineering work, so the salaries are hazy.

Ultimate Selection?

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

In case you love maths and understanding how algorithms work below the hood, then machine studying engineering is your finest wager.

In case you 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 approach, each roles pay effectively and have glorious long-term profession prospects.

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

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

See you there!