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So, you want to become an AI engineer
but are completely overwhelmed.
What do you actually need to learn? How
hard is it? And do you need to be good
at math?
Well, let's answer those questions.
[music]
Hello everyone and welcome back to my
channel. My name is Kirk and today we're
going to go over what it takes to become
an AI engineer.
My background, I'm a software engineer.
I've been doing AI work for maybe the
last two years and I'm going to tell you
what worked for me and what I think was
going to work for you. Because a lot of
times you go online and you're going to
see a big list of all these Python
libraries you need to learn. They're
going to tell you, "Hey, you need to be
good at math and you're going to be
completely overwhelmed."
And I get it. As I said, an AI engineer
is a software engineer. That's an
important distinction.
It's not going to be easy, but if you
take this path, it's not going to be as
hard as you think. My goal is to get you
to create real programs working with LMS
and AI agents, deploy them, and solve
real business cases. I work in Azure,
but you can pick the cloud provider you
want, and I work mostly with GPT.
But again gang, we're going to go over
all that stuff, but the first thing we
should do is figure out what an AI
engineer actually is.
What is an AI engineer? An AI engineer
is a software engineer who designs,
builds, and deploys applications that
use artificial intelligence models,
machine learning, and LMS to solve real
business problems. aka you are creating
intelligent systems that perform tasks
that typically require human
intelligence.
An AI engineer builds chat bots,
recommendation systems, and agents.
One important distinction we must make
is an AI engineer is not a machine
learning engineer. A machine learning
engineer focuses on the models
themselves.
aka an AI engineer builds the full
application while the ML engineer makes
the models work. Let's look at the steps
to becoming an AI engineer.
First, the fundamentals, learning
Python. But don't be overwhelmed. You
don't have to know everything. I'm going
to tell you exactly what you need to
know.
Next, LM basics. We're going to learn
about different models. We're going to
look at prompt engineering. You're going
to need to know what the difference
between parameters and hyperparameters
are. And then we're going to look at
using LMS with rags.
Next, agents. You'll want to build
agents. That's what everyone's looking
for right now. And you're going to need
to learn some frameworks. We'll look at
Lang Chang and Langraph. And in the
Microsoft ecosystem, learn about
Microsoft agent framework.
And last, multi- aent orchestration.
You're going to need to build multiple
agents. Have them talking to each other
using this this knowledge to build real
business use cases.
First, the fundamentals.
I'm going to make some assumptions. I'm
going to assume you have strong
foundations in computer science, data
structures, and algorithms. Also, you
know about version control and
specifically Git.
Now, you're going to have to learn
Python.
No question about it. I know that
Microsoft agent framework, the new
framework is available in C, but if you
want to be taken seriously as an AI
engineer, learn Python. But don't worry,
don't be overwhelmed. I'm going to tell
you exactly what you need.
So, let's talk about some of the
fundamentals of Python that you need to
know. You're going to need to know about
the variables and types. And since you
have a strong foundation in data
structures and object oriented
programming, you're going to have to
know about classes, inheritance, and
composition.
And you're going to have to know about
functions, default arguments, variable
positional arguments, and keyword
arguments. And you have to know about
lists, tpples, dictionaries, and sets,
decorators.
and also learn about virtual
environments. You're going to probably
do some stuff in Jupyter notebooks,
especially with using hugging face,
which I'll get to in a second, but learn
about virtual environments and also
package managers. Know about pip versus
UV. I use UV a lot. A lot of the
projects I've been on, we use UV.
Let's talk about some of the libraries
that I use all the time. Pantic is a
data validation and parsing library
using Python type hints. I use it all
the time. You're probably going to use
it a lot too, especially when we get
into prompt engineering. Another thing
we're going to have to talk about and I
know you come from a background,
hopefully you come from another language
and you know about restful APIs. I'm
always building my restful APIs in
Python using fast API. Just learn fast
API seems to be the most popular one
right now. So just jump right into it
and I know we talked about the fact that
AI engineers are different than ML
engineers and data scientists and pandas
is usually is a library that data
scientists use a lot. Pen is is used for
data manipulation for and for data
scientists, but I use it a lot for
reading CSV files and working with data
frames and working with data, especially
when you talk about testing, which of
course brings me to
unit testing and I use piest. You're
going to have to learn about unit
testing. Again, if you come from another
background, another programming
language, you probably know about unit
testing. Just learn piest. You're going
to be running those. You're going to be
using tests.
Next, LLM basics.
Okay, we just covered the fundamentals.
While you're learning the fundamentals,
learning Python, you can also start
learning the basics of LLMs.
And while you're learning about LMS, aka
learning about different models, how
inference works, you should also start
writing code.
Sounds scary, right?
Well, it isn't.
Why? Well, Hugging Face is a great place
to start.
Hugging Face is a company and open-
source platform for AI, NLP, and
multimodal models.
Think of it as GitHub plus app store for
AI models. Let's take a look at it right
now. Here we are on huggingface.co.
Now, here is where you can go and learn
about NLPs
and LLMs, the very basics. This is what
I did. I went to the LM course first and
I stepped through this and as I said, I
didn't say you need to know about
PyTorch or TensorFlow and Python
libraries because you're going to be
exposed to that a bit and you're going
to be working with Jupyter notebooks.
But this is a great way to learn about
transformers,
tokenizers,
and inference.
great foundations that you need to know.
I went through this course, great
course,
all the fundamentals. I don't do a lot
of this on my job, but you need to know
this stuff. And then after that,
I also did the where is it here?
I also did the agents course which is
going to get you exposed to different
agent frameworks specifically the one I
use the most now is lane chain and lane
graph and of course in the Microsoft
ecosystem Microsoft agent framework and
using LMS which I'll talk about in a
second as well but this is a great way
to learn about frameworks what's
available lin index small agents and
building a gentic Greg use case. Have
some fun. Play with this. Go through
this course. Get the fundamentals of
what agent frameworks and what agents
are. I should also state on the LM
course. You're going to also learn about
fine-tuning models. Again, the
fundamentals that you need. I don't do
this in my day-to-day job, but you
should know about how to fine-tune
models, be exposed. Another thing you're
going to learn with this LM course is
you're going to learn about models and
NL NLP tasks, specifically
summarization classification text
generation, and you're going to be
fine-tuning models. You're going to know
the difference between what a
pre-trained model is, a fine-tuned model
is. Again, I don't do a lot of this. I
know I have a tutorial on fine-tuning
models.
the foundations that you need to know.
Again, I'm gonna always say that. But
you're going to be exposed to that.
You're also going to learn about
hyperparameters and the difference
between hyperparameters and parameters.
Parameters being the size of the model
like 1.5 billion, 10 billion. Again,
this is stuff like you're going to learn
about open source models. And then of
course once you get in the open AI in
the Microsoft world you're going to know
about what
GPT is which is a prop proprietary
model. If you go back to the homepage
and click on models you're going to be
exposed to different different models
here classifying by the task by the size
and then looking at the model cards
themselves and not just LMS but
multimodal models as well.
Great place to get read up about models,
learning about different LMS. Hugging
face is your friend. Play with this.
Now we need to talk about prompt
engineering. Prompt engineering is the
practice of designing and refining
inputs aka prompts you give an AI model
so produces more accurate, useful, and
predictable outputs.
A lot of my time as an AI engineer is
just spent writing and editing prompts.
I work with business analysts, with
product managers, figure out the
business use cases, write some PC's,
and show them what we can do, and then I
just fine-tune the prompts.
Yep.
I'm just writing language, not code. But
that's part of being an AI engineer. But
this is really important. So get good at
it and get used to it. And that brings
us to our next topic.
Next, agents and multi- aent
orchestration.
Okay. Next, let's talk about agents and
agent orchestration.
First,
lang chain.
Okay. Let's talk about lang chain. Now I
know in my channel I'm always talking
about Microsoft and staying in the
Microsoft ecosystem and introduce
introducing you to Microsoft agent
framework. But for most of my projects
and to be an AI engineer, you should or
you have to know lang chain lane chain
and lane graph. And the best way to
learn that is just go to lanechain.com
which I'm at right now. And you see at
the top here, learn and you go to the
lane chain academy.
And I went through all this. I did this
a while ago. I think in 2024, so a year
and a half ago from this recording, but
this is a great resource. And I did the
foundations which you start building
agents. And I also learned about
Langsmith
which is about observability and seeing
how these models and these agents work
and how they reason.
Great tools. Learn this stuff. Of
course, if you're in the Microsoft
ecosystem, they also have very similar
tools for observerability, which we'll
talk about next. But just learn this
stuff. Learn lang chain, learn lang
graph, master this. And this, as I said,
is a great resource.
Um, click on here, you can go through
it. The only thing about this is you're
going to be in Jupyter notebooks a lot.
And again, as an a engineer, you want to
get out of Jupyter notebooks. if you
want to start building real
applications,
which we do using my tutorials, which we
will get to next.
And that brings me to YouTube and my
channel. Yes, maybe I'm biased, but I
think a great way to be an AI to become
an AI engineer is to go through some of
my content here. But where do you start?
I have a couple playlists here. The best
way to learn about the fundamentals of
Azure and what and Azure AI foundry but
now Microsoft Foundry is to go through
this playlist here. Let's just look at
the playlist.
I would go through here and step through
all the videos. Maybe you already done
that. Of course, you subscribed. You
probably have. But learning about LM
with rags at the start and learning
about well fine-tuning as I said I don't
really do my day-to-day job but you're
going to learn about agentic rag and
everything else that this has to offer
in terms of debugging your agents guard
rails. You're going to learn about how
to evaluate different models, what
models are available in Microsoft
Foundry and building multimodal and then
and then you're going to be using the
guey itself and learn about Microsoft
Foundry and workflows.
After that, you're going to want to know
about the different frameworks available
to building agents in Python. Now we
have Microsoft agent framework used to
be semantic kernel and autogen. I again
have a playlist here for that. Let's
look at the playlist itself.
So we I would just step through this
again. Maybe you already have but you
want to learn a framework. You want to
learn Microsoft agent framework. Okay
that covers it for what it takes to
become an AI engineer.
But as I said at the very start, an AI
engineer is also a software engineer. So
it's great to learn this stuff that I
just talked about, but you also need to
know how to be a great software
engineer.
Become a platform engineer.
Learn the cloud. Pick your cloud
provider of choice. For me, it's Azure.
And really get good at it.
learn what rate limiters are, API
gateways,
and also
we are all people, right? We sometimes
forget that. As I said when I talked
about prompt engineering, a lot of my
day is spent working with product
managers and business analysts figure
out what we actually need to build and
editing those prompts to create a real
product. something you can be really
proud of. And I hope you follow these
steps and take my advice, you'll really
get something out of it.
And if you did get something out of it,
also remember to please subscribe
[music] and like this video. And until
next time, I'll see you then.
So you want to become an AI engineer—but you’re overwhelmed by Python libraries and endless AI buzzwords. In this video, I break down what it actually takes to become an AI engineer, based on my experience as a software engineer working with AI in production. We’ll clearly define what an AI engineer is (and is not), the exact Python fundamentals you need, how to learn LLMs without drowning in theory, and how to build real-world applications using agents, RAG, and modern frameworks. You’ll learn: The difference between AI engineers and ML engineers Exactly which Python concepts matter (and which don’t) How to learn LLM fundamentals using Hugging Face Prompt engineering as a real engineering skill How to build agents with LangChain, LangGraph, and Microsoft Agent Framework Why cloud, APIs, and software engineering fundamentals still matter This roadmap is designed for software engineers and developers who want to transition into AI engineering without wasting time on the wrong things. 00:00 – Feeling overwhelmed by AI? Let’s fix that 00:35 – My background and why this roadmap works 01:48 – What is an AI engineer (vs ML engineer)? 02:42 – The learning roadmap: fundamentals → LLMs → agents 03:45 – Python fundamentals you actually need 05:26 – Core Python libraries: FastAPI, Pydantic, Pandas, Pytest 06:48 – Learning LLM basics while learning Python 07:23 – Hugging Face: the best place to start with LLMs 11:06 – Prompt engineering as a core AI engineering skill 12:08 – Agents, LangChain, LangGraph, and orchestration 14:03 – Microsoft Foundry, workflows, and real applications 16:19 – Why great AI engineers are great software engineers #AIEngineer #ArtificialIntelligence #Python #LLMs #PromptEngineering #LangChain #LangGraph #AIAgents #MicrosoftFoundry #AzureAI #HuggingFace #RAG #SoftwareEngineering #MachineLearning #FastAPI