Luyện nói tiếng Anh bằng Shadowing qua video: Is ML Engineering Dead? (AI vs ML Engineer)

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.

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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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Trong video này, bạn sẽ khám phá được sự khác biệt giữa kỹ sư AI và kỹ sư máy học, giúp bạn hiểu rõ hơn về hai lĩnh vực nghề nghiệp này. Mục tiêu chính là nâng cao khả năng nói tiếng Anh của bạn thông qua việc lắng nghe và thực hành với các thuật ngữ chuyên ngành trong công nghệ. Bạn sẽ nhận thấy rằng việc nắm vững các khái niệm này không chỉ giúp bạn trong sự nghiệp mà còn cải thiện kỹ năng giao tiếp hàng ngày của bạn.

Ngân hàng cụm từ

"AI engineer" - Kỹ sư AI
"machine learning engineer" - Kỹ sư máy học
"foundational models" - Các mô hình nền tảng
"neural networks" - Mạng nơ-ron
"deploying models" - Triển khai mô hình
"integrate them" - Tích hợp chúng
"cutting edge AI" - AI tiên tiến

Cải thiện điểm yếu của bạn

Đoạn video này cung cấp cho bạn những thông tin hữu ích về cách phát âm và nhịp điệu trong tiếng Anh khi nói về các khái niệm công nghệ. Một trong những điểm yếu thường gặp của người học tiếng Anh là phát âm không chính xác và thiếu sự tự tin khi giao tiếp. Việc luyện nói tiếng Anh qua phương pháp shadow speech hoặc shadowing tiếng Anh sẽ giúp bạn cải thiện kỹ năng phát âm và cách nhấn nhá từ ngữ.

Khi bạn thực hành shadowspeaks, hãy chú ý đến cách diễn đạt và sự nhấn nhá trong câu. Bằng cách này, bạn sẽ dễ dàng hơn trong việc ghi nhớ các thuật ngữ và cách sử dụng chúng trong ngữ cảnh thực tế. Đừng ngần ngại lặp lại các cụm từ đã học từ video để cải thiện khả năng nói của mình. Hãy xem video và luyện tập cùng với nó, đây là cách tuyệt vời để phát triển kỹ năng luyện nói tiếng Anh của bạn.

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Những cấu trúc người nói dùng nhiều nhất, kèm đúng cụm từ trong video:

Cấu trúc	Trong video
Mệnh đề quan hệ who / which + mệnh đề — thêm thông tin về người hoặc vật	someone who specializes · someone who actually · clear which is
Câu bị động be + quá khứ phân từ — nhấn vào việc xảy ra, không phải người làm	are trained · are also accredited · is required
Thì hiện tại hoàn thành have/has + quá khứ phân từ — việc đã xảy ra nhưng còn liên quan đến hiện tại	have worked · haven't been

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Shadowing là kỹ thuật học ngôn ngữ có cơ sở khoa học, ban đầu được phát triển cho chương trình đào tạo phiên dịch viên chuyên nghiệp và được phổ biến rộng rãi bởi nhà đa ngôn ngữ học Dr. Alexander Arguelles. Nguyên lý cốt lõi đơn giản nhưng cực kỳ hiệu quả: bạn nghe tiếng Anh của người bản xứ và lặp lại to ngay lập tức — như một "cái bóng" (shadow) đuổi theo người nói với độ trễ chỉ 1–2 giây. Khác với luyện ngữ pháp hay học từ vựng bị động, Shadowing buộc não bộ và cơ miệng phải đồng thời xử lý và tái tạo ngôn ngữ thực tế. Các nghiên cứu khoa học xác nhận phương pháp này cải thiện đáng kể phát âm, ngữ điệu, nhịp điệu, nối âm, kỹ năng nghe và độ lưu loát khi nói — đặc biệt hiệu quả cho người luyện IELTS Speaking và muốn giao tiếp tiếng Anh tự nhiên như người bản ngữ.

Phương pháp shadowing: đọc hướng dẫn từng bước đầy đủ →

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Phương Pháp Shadowing Là Gì?