Shadowing-Übung: Is ML Engineering Dead? (AI vs ML Engineer) - Englisch Sprechen Lernen mit Video

One of the most confusing questions in tech right now is what is the difference between a AI engineer

and a machine learning engineer?

Both are six-figure jobs, but if you choose the wrong one, you can spend months, if not years,

learning the wrong skills for that particular job that you are after.

As a practicing machine learning engineer, I want to outline the key differences between the two roles,

so that way you can make the correct choice for your career path.

Let's get into it.

In a nutshell, an AI engineer is basically a software engineer,

but someone who specializes in the use and integration of foundational Gen AI models,

like Claude, BERT and the regular GPT model.

They don't build these models directly from scratch but rather use them to serve a certain purpose.

On the other hand a machine learning engineer is someone who actually builds models from scratch

or using basic libraries and deploys them in end to end systems.

These models that a machine learning engineer builds are mainly more traditional machine learning models like gradient booster trees

or neural networks

but sometimes they can also work on gen ai models as

well what i find funny about this naming convention is

that machine learning is actually a subfield of ai

so an ai engineer at least by today's definition is actually a gen ai engineer

so as you can see it's not very crystal clear which is why the question is so confusing.

Anyway, enough of me being pedantic, let's explain what these roles do in a bit more detail.

As I just mentioned,

you should think of an AI engineer as essentially a software engineer with a specialism in using Gen AI models

and using those models more to build a product rather than

building these llms directly from scratch ai engineers mainly work with something called foundational models

which are really big neural networks that are trained on oceans of data

that is like text image audio and video the most kind of popular gen ai

or foundational model is obviously the ChatGPT with its GPT series.

As I said before, AI engineers don't build or train these foundational models.

They rather integrate them to traditional software products.

For example, they may embed a foundational model as a chatbot on a shopping website

so that customers can quickly find what they're after on

that particular website or they may add it to a coding IDE as a coding assistant like a cursor is.

So what skills do you need to become an AI engineer?

Well this role is evolving very quickly and

so the requirements change pretty much every quarter

but let me give you a list of all the key things that you need to know.

What you mainly need to kind of be aware of or at least learning is all the latest developments in LLMs,

neural networks and pretty much just how the AI industry is moving.

To give you a concrete list, you should have solid software engineering skills,

skills in Python, SQL and other back-end languages like Java or Go,

CICD, Git and GitHub, LLMs and Transformers, RAG, prompt engineering, foundational models,

fine tuning and the model context protocol.

So as I said, it's basically all the fundamental software engineering skills, plus that extra knowledge about Gen AI,

LLMs, and basically all the cutting edge AI that's currently happening at the moment.

If you are looking to become an AI engineer, then I recommend you check out the AI engineer career track from 365 Data Science,

who are kindly sponsoring this video.

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It will teach you the basics of Python, how to work with data using pandas, what are LLMs and how do they work, how to use vector databases,

and further courses that you can elect to further solidify your understanding on a range of topics like model deployment, machine learning, and deep learning.

You will also develop portfolio projects along the way to solidify your understanding.

Learners from 365 Data Science now work at top tech companies like Amazon, Meta and Google.

So you'll be in great hands.

Not to mention, you'll get an AI engineer certificate that you can showcase to prospective employers.

These certifications are also accredited by leading industry bodies, making your application stand out even more.

I will leave all of this linked in the description below for you to check out.

A machine learning engineer focuses on building machine learning models

and then deploying them into production systems systems it initially came

from the software engineering role where there was demand for software

engineers with machine learning skills kind of similar how there is

demand now for software engineers with ai engineering skills the significant distinction between a machine learning engineer

and an ai engineer is that a machine learning engineer builds machine learning models directly from scratch.

However, these models are more specific and narrower in exactly what they solve.

For example, you may build a machine learning model

that detects credit card fraud or a recommendation system for a social media app.

But as you can see, these models are very targeted and have a very specific use case.

Whereas the models an AI engineer works with are generative

and more foundational and

because they're foundational they can be used on a wide variety

of tasks like chat gpt it can be used for loads of things

but the models built by machine learning engineers like i just said a lot more specific

so there is a difference in the types of models

and the use case of models between the two roles as well there also exists further specialism

and further distinction in the machine learning engineer role For example, you could also be a machine learning hardware engineer, a machine learning platform engineer,

or a machine learning infrastructure engineer.

These roles are kind of out of the scope for this video, and these roles are something that you kind of get into after like five years in the field.

And like I said, they're kind of out of scope, but I'm just mentioning them here so that you're aware of it.

As a practicing machine learning engineer myself, I can tell you the exact things that you need to know.

So obviously to start, you need to know Python and SQL.

However, some companies may require other languages.

For example, in my current role, I'm also learning Rust.

You also need to know Git and GitHub, Bash and Z shell, AWS,

Azure and GCP, software engineering fundamentals like CICD, MLOps and Docker,

excellent machine learning knowledge ideally with a specialism in an area like forecasting recommendation systems

or computer vision and solid mathematical understanding of statistics linear algebra

and calculus so as you can see you basically need software engineering skills

but also this kind of wide skill set in machine learning

and maths which makes sense from how i described the distinction between AI engineering and machine learning engineer.

So the question still stands, which one should you choose?

Well let me break down some of the more logistical aspects.

The background for both roles is pretty similar.

Ideally you have a master's in a stem based subject

and you have worked a couple of years as either a data scientist or software engineer.

If you want to become an AI engineer, the software engineer background is probably better than a data scientist.

But I wouldn't worry too much about this.

I will say though that AI engineering is slightly easier to get into,

mainly because learning how to use Gen AI is a slightly easier learning curve than understanding all the complex maths

and machine learning theory that is required to become a machine learning engineer

but that is just my opinion and i'm sure other people may disagree with

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that machine learning engineering is the more established role but that's mainly

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because gen ai foundational models haven't been around for that long

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and the current demand or the popularity of ai is increasing

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so the demand for ai engineers is currently skyrocketing you do need to be careful though

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because titles in this industry from first-hand experience are very vague

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for example i have people working in my company who are machine learning engineers

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but they specifically and only work on gen ai

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and foundational models yet their title is machine learning engineers

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or machine learning engineer but they do an ai engineer job

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so i wouldn't necessarily focus on titles too much

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but rather focus on the type of work that you'll be doing

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and make sure you read the job description that is the key thing

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when it comes to pay according to levels fyi the median salary of machine learning engineer

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in the uk is 105 000 pounds

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and for an ai engineer is 75 000 pounds

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but i would take this with a big pinch of salt

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because these job titles are mainly large tech companies

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and as i just mentioned titles can be misleading

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and it's kind of a blurry line of what classes as machine learning engineer and AI engineer.

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So I wouldn't base your career decision simply on this salary.

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Either way, both pay pretty well regardless.

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So what is your final choice?

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In my opinion, just go with what you're most interested in and what you're drawn to the most.

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That's how I became a data scientist and ultimately a machine learning engineer.

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If you love maths

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and understanding how things work under the hood then machine learning engineering is clearly the choice for you

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but let's say you're really interested in this ai kind of wave at the moment

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and you've been doing a lot of reading in that specific area

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and you really enjoy just shipping products that are ai focused

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and you don't care too much about the underlying maths then obviously an ai engineer

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or an ai engineering career track is best for you

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but as i said both jobs are clearly in high demand the demand is only growing they pay you well

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so either choice you make you really can't go wrong however suppose you do feel

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that greater pull to become a machine learning engineer

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if that's the case then i recommend you check out this

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video where i explain exactly how i would become a machine learning engineer

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if i was starting again i'll see you there

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Über diese Lektion

In dieser Lektion übst du, komplexe technische Inhalte in Englisch zu verstehen und zu wiedergeben. Der Video-Dialog klärt die Unterschiede zwischen AI-Engineern und Machine-Learning-Engineern auf – ideal, um deine Sprachfertigkeiten in einem aktuellen, fachlichen Kontext zu trainieren. Du wirst nicht nur neue Vokabel lernen, sondern auch die Fähigkeit entwickeln, flüssig zu sprechen, auch wenn der Text schnell und informativ ist.

Wichtige Vokabel und Redewendungen

Foundational model – Grundmodell (z. B. GPT, BERT)
End-to-end system – End-to-End-System (vom Entwurf bis zur Implementierung)
Fine-tuning – Feineinstellung (Anpassung von Modellen an spezifische Aufgaben)
Prompt engineering – Prompt-Engineering (Optimierung von Eingaben für KI-Modelle)
Deploy – einführen, implementieren (z. B. Modelle in ein System integrieren)

Übungstipps für Shadowing

Der Sprecher im Video spricht mit einer klaren, aber flüssigen Geschwindigkeit – perfekt fürs Englisch Shadowing. Folge diesen Schritten, um deine englische Aussprache zu verbessern:
1. Höre zuerst einen kurzen Abschnitt (5–10 Sekunden) genau an. Achte auf Intonation und Betonung von Fachbegriffen wie "machine learning engineer".
2. Wiederhole sofort hinterher – versuche, die gleiche Tempo und Klangfarbe zu erreichen. Nutze ein shadowing site, um dir die Aufnahme zu merken und zu vergleichen.
3. Konzentriere dich auf Redewendungen wie "in a nutshell" oder "on the other hand" – diese helfen, die Struktur des Dialogs zu verstehen und zu wiedergeben.
4. Übe täglich 10–15 Minuten – shadow speech ist eine bewährte Technik, um flüssiger zu werden. Mit der Zeit wirst du merken, dass du komplexe Sätze ohne Pause wiedergeben kannst.
Denke: Shadowspeaks funktioniert am besten, wenn du dich voll auf den Sprecher einlässt – kopiere nicht nur Wörter, sondern auch die Emotion und den Rhythmus.

Grammatik in diesem Video

Die Strukturen, die der Sprecher am häufigsten verwendet, mit den genauen Worten aus dem Video:

Struktur	Im Video
Relativsätze who / which + Satz – eine Zusatzinformation über eine Person oder Sache	someone who specializes · someone who actually · clear which is
Passiv be + Partizip Perfekt – wichtig ist, was geschieht, nicht wer es tut	are trained · are also accredited · is required
Present Perfect have/has + Partizip Perfekt – eine vergangene Handlung, die jetzt noch wichtig ist	have worked · haven't been

Was ist die Shadowing-Technik?

Shadowing ist eine wissenschaftlich fundierte Sprachlerntechnik, die ursprünglich für die professionelle Dolmetscherausbildung entwickelt und durch den Polyglotten Dr. Alexander Arguelles populär gemacht wurde. Die Methode ist einfach aber wirkungsvoll: Du hörst englisches Audio von Muttersprachlern und wiederholst es sofort laut — wie ein Schatten, der dem Sprecher mit nur 1–2 Sekunden Verzögerung folgt. Anders als passives Hören oder Grammatikübungen zwingt Shadowing dein Gehirn und deine Mundmuskulatur, gleichzeitig echte Sprachmuster zu verarbeiten und zu reproduzieren. Studien zeigen, dass es Aussprachegenauigkeit, Intonation, Rhythmus, verbundene Sprache, Hörverständnis und Sprechflüssigkeit signifikant verbessert — was es zu einer der effektivsten Methoden für die IELTS Speaking-Vorbereitung und reale englische Kommunikation macht.

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Wichtige Vokabel und Redewendungen

Übungstipps für Shadowing

Grammatik in diesem Video

Was ist die Shadowing-Technik?