Shadowing Practice: Is ML Engineering Dead? (AI vs ML Engineer) - Learn English Speaking with 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.

This career track will teach you everything you need to know to land a career as an AI engineer, even if you're a complete beginner.

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.

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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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Vocabulario y notas de pronunciación para esta lección

Esta lección de conversación de nivel B2 se basa en el vídeo “Is ML Engineering Dead?”. Las palabras que más se repiten: engineer, learning, machine, model, software. Este vídeo tiene 136 frases y 1774 palabras para practicar shadowing. La parte hablada dura 11:56. El hablante mantiene un ritmo constante de unas 149 palabras por minuto, cómodo para el shadowing. El 82 % de las palabras está entre las 3.000 más comunes del inglés; conviene repasar el resto antes de empezar.

Vocabulario clave de este vídeo

Las 15 palabras más avanzadas del vídeo, con su pronunciación y significado:

Palabra	Pronunciación	Significado
mathematics sustantivo	/mæθ(.ə)ˈmæt.ɪks/	matemáticas, matemática
scratch verbo	/skɹæt͡ʃ/	rascar
neural adjetivo	/ˈnʊɹəl/	neural
distinction sustantivo	/dɪˈstɪŋkʃən/	distinción
solidify verbo	/səˈlɪdɪˌfaɪ/	solidificar
confuse verbo	/kənˈfjuːz/	confundir, barahustar
deploy verbo	/dɪˈplɔɪ/	desplegar
ideally adverbio	/aɪˈdi.ə.li/	idealmente
recommendation sustantivo	/ˌɹɛkəmɛnˈdeɪʃən/	recomendación
accredit verbo	/əˈkɹɛd.ɪt/	acreditar
blurry adjetivo	/ˈblɜːɹi/	borroso, desenfocado
chatbot sustantivo	/ˈtʃætbɑt/	bot conversacional, chatbot
cursor sustantivo	/ˈkɜːsə/	cursor
generative adjetivo	/ˈd͡ʒɛnəɹətɪv/	generativo
gradient sustantivo	/ˈɡɹeɪdiənt/	gradiente

Phrasal verbs que vas a escuchar

Palabra	Significado
check out verbo	investigar, chequear
break down verbo	descomponerse, averiarse

Gramática en este vídeo

Las estructuras que más usa el hablante, con las palabras exactas del vídeo:

Estructura	En el vídeo
Oraciones de relativo who / which + oración — información extra sobre una persona o cosa	someone who specializes · someone who actually · clear which is
Voz pasiva be + participio pasado — importa lo que ocurre, no quién lo hace	are trained · are also accredited · is required
Present perfect have/has + participio pasado — una acción pasada que sigue importando ahora	have worked · haven't been

Pronunciación a tener en cuenta

El hablante usa 24 contracciones y formas reducidas, como you're, don't, I'm. Dilas en su forma corta, tal como las oyes.

Los sonidos de “th”: mathematics /mæθ(.ə)ˈmæt.ɪks/, mathematical /ˌmæθ(.ə)ˈmæt.ɪ.kəl/
Los sonidos de “sh” y “zh”: foundational /faʊnˈdeɪ.ʃə.nəl/, distinction /dɪˈstɪŋkʃən/, recommendation /ˌɹɛkəmɛnˈdeɪʃən/, nutshell /ˈnʌt.ʃɛl/, specialize /ˈspɛʃəˌlaɪz/
Palabras largas — cuida el acento: foundational /faʊnˈdeɪ.ʃə.nəl/, mathematics /mæθ(.ə)ˈmæt.ɪks/, solidify /səˈlɪdɪˌfaɪ/, ideally /aɪˈdi.ə.li/, recommendation /ˌɹɛkəmɛnˈdeɪʃən/

Sonidos difíciles para hispanohablantes:

s + consonante al inicio — sin añadir una “e” delante: scratch /skɹæt͡ʃ/, skyrocket /ˈskaɪˌɹɒk.ɪt/, specialize /ˈspɛʃəˌlaɪz/, sponsor /ˈspɒn.səː/
/v/ — no es /b/: los dientes tocan el labio inferior: generative /ˈd͡ʒɛnəɹətɪv/, evolve /ɪˈvɑlv/, vague /veɪɡ/, prospective /pɹəˈspɛktɪv/, vector /ˈvɛktɚ/
/z/ — sonora, no /s/: confuse /kənˈfjuːz/, specialize /ˈspɛʃəˌlaɪz/

Cómo practicar con este vídeo

Escucha el vídeo entero una vez sin hablar y anota las palabras que no conoces.
Haz shadowing frase por frase a velocidad normal, repitiendo cada una hasta que tu ritmo coincida con el del hablante.
Grábate y compara con el original, prestando atención a palabras como mathematics, scratch, neural.

¿Qué es la Técnica de Shadowing?

Shadowing es una técnica de aprendizaje de idiomas respaldada por la ciencia, desarrollada originalmente para la formación de intérpretes profesionales y popularizada por el políglota Dr. Alexander Arguelles. El método es simple pero poderoso: escuchas audio en inglés nativo y lo repites en voz alta de inmediato, como una sombra que sigue al hablante con solo 1-2 segundos de retraso. A diferencia de la escucha pasiva o los ejercicios de gramática, el shadowing obliga a tu cerebro y músculos de la boca a procesar y reproducir simultáneamente patrones de habla reales. Las investigaciones muestran que mejora significativamente la precisión de la pronunciación, la entonación, el ritmo, el habla conectada, la comprensión auditiva y la fluidez al hablar, convirtiéndola en una de las metodologías más efectivas para la preparación del IELTS Speaking y la comunicación en inglés en el mundo real.

Técnica de shadowing: lee la guía completa paso a paso →

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