Практика Shadowing: Fastest way to become an AI Engineer in 2026 - Изучайте разговорный английский по видео

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you're looking to become an AI engineer

вы хотите стать инженером по искусственному интеллекту

if this job is on your radar then this video is

all you need it's a full breakdown of what this role is how it differs from software engineering

and machine learning engineering and how to become one

so I'm going to give you a full roadmap what projects

you should be doing what kind of person this job would suit

and also my opinion on where see this role going

and is it worth your time to invest in okay before

we go any further i got into tech in my 30s i learned from scratch all this stuff

and i watched loads of videos like this i'm not just

going to give you a random roadmap you can get off chat gbt

when i was learning i wish someone taught me this stuff

but also said you can go out there whilst you're learning

and build your own products and sell them out there in the world

so you can learn to become an ai engineer and you can also make a SaaS.

Make something, sell it.

Worst case scenario, it looks good at an interview.

Best case scenario, it becomes a business.

So I'm here, my page, everything on my content is all about encouraging you guys to learn technology, but also believe in yourself,

sell your own stuff and see where it goes.

Because taking a chance might lead somewhere.

So this video is going to be four steps.

Each step is going to gradually increment.

At the end of it, I'm going to encourage you to make your own micro SaaS.

And if you're new here, my name is Andrew.

I'm a senior software engineer and I got into tech in my 30s totally from scratch

and I have worked in the AI space as an engineer.

So I've watched this role evolve over the last few years.

I make tech content around your career, around building apps.

So if that sounds like your kind of vibe, subscribe if you want.

Okay, before we go though, I want to just manage expectations because firstly, I want to give you correct factual information on this channel.

The chances of you getting your first role working for OpenAI

are very very low unless you look at further education so getting a master's or a PhD.

There are a lot of jobs below that there are

so many opportunities in AI below that so that should be your focus.

So clearly the reason this is taking off

and the reason the salaries are so high is that companies

and the world is crying out for people who can implement AI.

Okay firstly though, what is an AI engineer?

This is someone who builds production-ready systems using pre-trained AI models and APIs.

And to further set the scene here, let's talk about three roles.

So there's a software engineer, there's an AI engineer, and there's a machine learning engineer.

People confuse these last two a lot.

So a software engineer will write code or get AI to write the code nowadays, and that will produce a program.

Machine learning engineers, they take loads of data, they clean the data they fine-tune the model to create a program

or a model that will achieve some output the AI engineer makes

that model useful in the real world

so they'll make it into a real system or a real app okay

so this is not a machine learning engineer this is not

a machine learning researcher these are people deep in math people

who write research papers they train the models themselves this is not

that this is more like the practical use of AI so it's what comes after.

If it's still not quite clear, there's a really good article called the Rise of the AI Engineer, where they basically say that in the future, there's going to be more AI engineers than ML engineers.

There's a really good visual and it shows the line of an API.

On the left side, you've got the data, the research, the data science side.

On the right side, this is where the AI engineer comes in.

So this is the product and user focus.

You've got full stack engineers there so it's all about chains agents and making things basically.

With that said let's get into the roadmap.

So I've broken it down into four steps and how you learn this stuff you learn by doing.

You don't learn by watching tutorials or reading documentation you learn by becoming a problem solver.

So it's going to be four steps at the end of each step there'll be a project

so this is all project-based learning.

Step one is to focus on the core skills of AI engineering.

So Python, LLMs, and system thinking.

And to start with, everyone starts with Python.

This is the language of choice for AI and machine learning.

The community has chosen it.

You have R as well, but you have a lot of things built on top of Python, a lot of the libraries which just make it easier.

For example, PyTorch.

And if you've never coded or you've never learned a programming language before, these are some things which help me.

And the good mindset is that you're going to be learning Python for the rest of your career probably.

It's not something you're learning like a year.

It's just a continuous process.

But the real thing you're learning is problem solving.

And this is hard to teach.

What helped me was doing toy problems.

So there's a site called Code Wars.

It's totally free.

Start right at the beginning.

And whenever you're learning, try and start every session by solving one of these small problems in Python.

Also, only watch one video tutorial on Python and then build and use documentation.

So if you go courses, you could do YouTube courses.

Those are free Code Camp ones.

There's a BroCode one.

If you want a paid one and the one I learned from, it's zero to mastery.

They've got a really good structure, really good projects, and that's how I learned Python myself.

But the mindset is becoming a problem solver, so you want to get uncomfortable and learn all the basics of Python.

And also Git and GitHub.

So what you want to do is these projects I suggest, you want to be committing them to Git.

So you don't have to know everything about Git, it's just the main things like pushing, pulling, and merging.

There's a site called Learn Git Branching, which is quite good.

There's a few YouTube tutorials you can watch, and also a more advanced book called ProGit.

So these are all free resources.

But again, Git's the kind of thing.

Learn it early on because you want to be committing all your stuff because this is the workflow that real engineers use.

So learn Git as soon as you can.

Okay, next we're actually getting into the real AI engineering.

So we're going to call and go through OpenAI's API documentation.

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There's loads there.

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They're the biggest provider.

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So it's a good start for you.

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This is like one of the key parts of being an engineer.

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So calling APIs, doing auth, requests, how to handle data, what data you get back.

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There's actually a fair amount involved with like gluing APIs.

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And it's something that took me a while to get to grips with.

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So yeah, OpenAI is a great start.

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They've got loads of documentation.

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So get stuck in with the Python part.

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Okay, the next part of the fundamentals is learning about the LLM basics and prompt engineering.

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So this is something

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that all software engineers are having to learn right now basically

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how to use an LLM how to use AI effectively so the context window, temperature, tokens, input and output patterns,

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learning all this stuff you'll need it in this role.

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Thank you to Datacamp for sponsoring this part of the video.

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Now I've said this loads of times but I taught myself the code

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and got into tech at 30

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and investing in my learning of some of these tech skills

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was without doubt the best return on investment of my whole life.

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And if you are serious about about becoming an AI engineer this year.

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The fastest path is not just binge watching tutorials, it's building real systems.

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This is why I recommend DataCamp, because it's really hands-on.

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They've got short lessons, in-browser coding, guiding projects, and instant feedback.

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And for most of you watching, you wanna look at this one here.

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It is the Associate AI Engineer for Developers track.

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Why?

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Because it uses the exact stack that you'll use to ship AI features, like the OpenAI API, Hugging Face,

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Langchain, Pinecone, Prompt Engineering, Embeddings, and LLM ops.

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So you're learning how to build chatbots, semantic search and production ready AI apps, not just playing with prompts.

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And if you're coming from a data background, then Datacamp also has this, the Associate AI Engineer for Data Scientist track.

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And this one's interesting because it goes deeper into PyTorch, fine tuning models like Lama 3, MLOps and deployment.

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And what's really cool is that these tracks prepare you for industry recognized Datacamp AI Engineer certifications.

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So basically you get in the best of both worlds, practical experience but also a pathway to earning these industry recognized certifications in one place.

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So if you want one structured, practical roadmap instead of just guessing what to learn next, check out Datacamp using the link in the description.

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And wherever you're starting from there will be a track for you and if you do check it out, hope you enjoy it.

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Alright, the last part of the fundamentals is to start thinking in terms of systems.

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What do I mean by this?

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So think of a small AI workflow and just kind of sketch it out

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so you have an idea of how all the pieces fit

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together in terms of like a big picture bird's eye view or like a tiny agent framework.

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So just sketch it out just so you have an understanding of the big picture.

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This will really help you going forwards.

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For the final step I mentioned we're going to do a project

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which is going to bring everything together that you've learned

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and it's going to be a personal research assistant CLI using Python and also calling the OpenAI API.

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So basically you'll ask it questions and it will structure the answers but also you can ask follow-up questions.

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So this is a nice project you can use git you can use python you're gonna call the openai api

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and put it all together that you've learned quick pause

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if you're enjoying this content

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if you're getting any value from it it's totally free for you i would massively appreciate it

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if you like the video and subscribe

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if you want it would help me out massively motivates me

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to make more videos like this to encourage you guys to build to learn

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and to use technology to change your life as it has done for me.

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Back to the video.

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Okay, step two.

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Now we're getting into the nitty-gritty, the real stuff.

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So back-end, architecture, and rag.

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So we're going to learn how to build real back-ends with FastAPI and Pedantic.

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So if you're a software engineer now, maybe you know this already and you can just add on these new technologies.

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Or if you're a computer science student, maybe you use Java, whatever, then just shifting, learning back-end, but learning Pedantic and FastAPI.

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And particularly things like async endpoints,

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background jobs, dockerize services, kind of key fundamental parts of backend working as an AI engineer.

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We're going to pick up Postgres.

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So Postgres, really popular.

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I learned it a couple of years ago.

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There's loads of resources on YouTube.

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You won't struggle to find resources for free on this.

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We're going to look at queues, migrations, event-driven patterns, and just get into grips with the database technology.

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You can actually build a decent amount of stuff with just step one.

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But step two is all about real backend services.

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So not local, all right?

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Because when you go into a real job, they're going to be using a lot of different things in the environment.

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They're going to have a database.

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They're going to use Docker probably.

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And obviously, backend, like FastAPI.

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So getting really deep into this.

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And I spend a lot of time in this area.

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And after Postgres, at this point in step two, we're going to learn RAG.

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Learn RAG end-to-end.

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Fundamental concept.

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Going to be using it a lot.

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So embeddings, vector databases, chunking, all this kind of stuff.

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There's a really good two hour, I think, free CodeCamp tutorial, completely free, I used a year or two ago.

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It's really good.

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And the whole goal of section two is to design

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and implement an AI backend using FastAPI that talks to a database using Postgres and has custom data sources.

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So the project, so every step will have a project.

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For this one, it's going to be a docs Q&A backend.

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So we use a fast API service where you can basically upload PDFs and markdown notes.

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Then we'll chunk, embed, and store in a vector database.

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So that will use Docker, which again, you can use free CodeCamp to learn.

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It's one of the most used technologies in tech.

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You will not struggle to learn it.

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And also a Postgres database.

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Okay, next, we're on to step three.

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And we're getting deep into this learning actual real skills, which are valuable in production-ready AI systems like monitoring, evals, and safety.

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Because AI systems can be tricky.

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This is not just step-by-step programming.

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They can hallucinate.

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They can just add in randomness.

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So we need a way to measure the cost, the efficiency, and reliability of our AI systems.

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So this is what we're doing now.

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Okay, first we add in observability and tracing.

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And there's a tool called Langtuse for this.

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It's open source.

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And what that will do is that every LM call we make, It will log the inputs, outputs, the latency, and the cost.

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So we can evaluate as we go on.

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We can see how effective it is and try and reduce that randomness in those hallucinations.

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Next is the safety element.

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So we add in guardrails.

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For example, prompt injection defenses so hackers can't trick a bot.

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Or filters to catch and redact personal information.

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Output validation to make sure responses follow our format.

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And also safety rules to block harmful content.

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So there's a tool called Sentry as well, which can catch those runtime crashes.

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And this is key because this is what differentiates real AI engineers who work with production ready apps.

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So if you can talk about this in an interview, the goal is to measure the quality of our system with numbers

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so we can catch regressions before a user would notice and just keep the whole system reliable and safe.

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Okay, and the project for step three, we're just going to take what we made in step two, the docs Q&A and just make it production ready, add in what we've learned in this step.

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So we're going to layer on lang fuse tracing

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so we can see every lm call we're going to add in the test data set

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so just add in like 50 or

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so real questions we're going to hook up lm as judge scoring

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so gpt4 will automatically just grade the responses add in the stuff we learned

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so prompt injection filters redacting personal data and add in sentry as well

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so we can just catch any crashes we get and bum

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that is a production ready app and that will look quite good at interview

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okay step four is deployment so getting this real production ready ai system

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that we've built and getting out there in the wild

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and there's a lot to this it's cicd

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which you you're gonna have to learn it's cloud it's just all the little things involved with making something locally

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and then shipping it getting out there in the world making

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it reliable making it the infrastructure good let's get into the steps

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and how we do this is first you want to pick a cloud provider

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so i recommend aws you can also do Azure, learn about the platforms, whichever one you choose.

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This is going to be a key part of the role.

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Obviously, if you're a backend developer, you probably know about this already.

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Then get your Dockerized backend running there with proper HTTPS, environmental variables, and also logging setup.

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Next, you want to get into CICD.

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Most companies, most web apps nowadays are set up with this kind of pipeline, unless they're really backwards, and it will automatically deploy without you having to do anything.

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So you can also add in monitoring, health check endpoints, alerts when things break, and also just to keep an eye on costs.

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And the goal with this step overall is to show an employer, an interview that you can independently build,

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deploy, and run a production-grade AI application without needing your hand-holding.

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So the way you do this is just ship a few of them.

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I recommend three.

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And yeah, get this out there.

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Put it in a GitHub repo.

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Put it out there for people to see showcase yourself show of everything you've learned

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so lms apis rag you know a good clean back-end structure

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with monitoring it's all about showing yourself off showing good code

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and applying for jobs okay to finish we're gonna make the last project on step four

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and we're gonna bring it all together and i think

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if you can go to an interview

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and show off this project it looks pretty cool

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so it's gonna be a production ready ai micro sass what

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we're going to do is take the docs QA bot we made, add on a simple web UI so people can see it in a browser

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so just use something like Gemini which is good for front ends.

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That's good because it'll teach you more about front end as well.

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We're going to add in user authentication so it's not wide open, a pricing gate with a free tier but also paid API limits too.

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Then deploy it all with CICD which we learned earlier with health checks, some basic metric dashboard, a public landing page as well documentation

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so you can go to an interview and this is a real product

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but also you can maybe even charge for yourself so

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when you're making this think of something that you actually want to build something

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which has like a production ai micro sass and just try

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and build it okay and that's the steps

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so now you're in the position where you can apply for an ai engineer role

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but also you can actually build your own ai micro sass

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so hopefully this video helped i didn't want to make a random video of a roadmap chat gpt can provide

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that for you but if you go through these steps

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and appreciate that this is not just like you don't learn python

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and then move on you're constantly learning these things and

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if you keep improving someone will take a chance on you

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you might have to be pragmatic with your first job i

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was i started off as a wordpress developer then was a software engineer in an ai startup

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so hope that helps

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if you got value from this video only thing i ask i appreciate it

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if you like the video and subscribe if you want happy coding i'll see you in the next one ciao

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Лексика и советы по произношению к этому уроку

Этот урок разговорной практики уровня B2 построен на видео «Fastest way to become an AI Engineer in 2026». Чаще всего повторяются слова: engineer, step, learning, learn, project. В этом видео 278 предложений и 3349 слов для шедоуинга. Речь длится 16:54. Говорящий говорит быстро, около 198 слов в минуту, поэтому звуки часто сливаются и сокращаются. 83% слов входят в 3000 самых частых слов английского языка; остальные стоит разобрать перед занятием.

Ключевая лексика этого видео

Самые сложные слова из видео (15), с произношением и значением:

Слово	Произношение	Значение
tutorial существительное	/ˌtjuːˈtɔːɹɪəl/	посо́бие, уче́бник
prompt глагол	/pɹɑmpt/	побужда́ть, внуша́ть
microwave существительное	/ˈmaɪkɹoʊˌweɪv/	микроволна́
sass существительное	/sæs/	де́рзость
deploy глагол	/dɪˈplɔɪ/	развёртывать, разверну́ть
subscribe глагол	/səbˈskɹaɪb/	подпи́сывать, подписа́ть
endpoint существительное		конец промежутка
redact глагол	/ɹɪˈdækt/	редакти́ровать, цензури́ровать
sentry существительное	/ˈsɛn.tɹi/	часово́й, карау́льный
workflow существительное	/ˈwɝkfloʊ/	пото́к рабо́т, воркфлоу
chunk существительное	/t͡ʃʌŋk/	кусо́к
firstly наречие	/ˈfɜɹstli/	во-пе́рвых, пре́жде всего́
mindset существительное	/ˈmaɪn(d)ˌsɛt/	склад ума́, о́браз мы́слей
deployment существительное	/dɪˈplɔɪmənt/	размеще́ние
certification существительное	/ˌsɜː.tɪ.fɪˈkeɪ.ʃən/	сертифика́ция

Фразовые глаголы, которые вы услышите

Слово	Значение
check out глагол	проверя́ть, прове́рить
get off глагол	спуска́ться, спусти́ться
go out глагол	выходи́ть, вы́йти
pick up глагол	подбира́ть, подобра́ть
show off глагол	рисова́ться, красова́ться
take off глагол	снима́ть, снять

Фразы, которые стоит повторять

Короткие законченные фразы из видео, которые пригодятся в повседневном разговоре:

With that said let's get into the roadmap.
It's not something you're learning like a year.
What do I mean by this?

Грамматика в этом видео

Конструкции, которые говорящий использует чаще всего, с точными словами из видео:

Конструкция	В видео
Условные предложения if + предложение, will/would + глагол — условие и его результат	if you want it would help · if you keep improving someone will take · if you want happy coding i'll see
Present Perfect have/has + причастие прошедшего времени — прошлое действие, важное сейчас	have worked · I've watched · I've broken
Придаточные определительные who / which + предложение — уточнение о человеке или предмете	people who can · someone who builds · Branching, which is

Произношение, на которое стоит обратить внимание

Говорящий использует 84 сокращённых и редуцированных форм, например you're, we're, you've. Произносите их коротко, так, как слышите.

Звуки «th»: authentication /ɔːˌθɛntɪˈkeɪʃn̩/, authorization /ˌɔːθəɹaɪˈzeɪʃən/, pathway /ˈpæθˌweɪ/, mathematics /mæθ(.ə)ˈmæt.ɪks/
Звуки «sh» и «zh»: documentation /ˌdɑkjəmɛnˈteɪʃən/, certification /ˌsɜː.tɪ.fɪˈkeɪ.ʃən/, injection /ɪnˈd͡ʒɛk.ʃən/, authentication /ɔːˌθɛntɪˈkeɪʃn̩/, dashboard /ˈdæʃˌboɹd/
Длинные слова — следите за ударением: documentation /ˌdɑkjəmɛnˈteɪʃən/, certification /ˌsɜː.tɪ.fɪˈkeɪ.ʃən/, developer /dɪˈvɛləpɚ/, asynchronous /eɪˈsɪŋ.kɹə.nəs/, authentication /ɔːˌθɛntɪˈkeɪʃn̩/

Звуки, трудные для русскоязычных:

/w/ — губы округлены, это не /v/: microwave /ˈmaɪkɹoʊˌweɪv/, workflow /ˈwɝkfloʊ/, whichever /wɪt͡ʃˈɛvɚ/, backward /ˈbækwɚd/, pathway /ˈpæθˌweɪ/
/h/ — лёгкий выдох, а не русское «х»: hallucinate /həˈl(j)uːsɪneɪt/, hallucination /həˌluːsɪˈneɪʃən/, hacker /hækəɹ/, harmful /ˈhɑɹmfl̩/
Звонкие согласные в конце слова — не оглушайте их: microwave /ˈmaɪkɹoʊˌweɪv/, subscribe /səbˈskɹaɪb/, nowadays /ˈnaʊ.ə.deɪz/, dashboard /ˈdæʃˌboɹd/, binge /bɪnd͡ʒ/

Как заниматься с этим видео

Прослушайте всё видео один раз молча и выпишите незнакомые слова.
Начните со скорости 0,75×, повторяйте предложение за предложением и вернитесь к обычной скорости, когда станет легко.
Запишите себя и сравните с оригиналом, обращая внимание на такие слова, как tutorial, prompt, microwave.

Что такое техника Shadowing?

Shadowing — это научно обоснованная техника изучения языка, изначально разработанная для подготовки профессиональных переводчиков и популяризированная полиглотом доктором Александром Аргуэльесом. Метод прост, но эффективен: вы слушаете аудио на английском от носителей языка и немедленно повторяете вслух — как тень, следующая за говорящим с задержкой в 1–2 секунды. В отличие от пассивного прослушивания или грамматических упражнений, Shadowing заставляет мозг и мышцы рта одновременно обрабатывать и воспроизводить реальные речевые паттерны. Исследования показывают, что это значительно улучшает точность произношения, интонацию, ритм, связную речь, понимание на слух и беглость речи — что делает его одним из самых эффективных методов для подготовки к IELTS Speaking и реального общения на английском.

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