DON'T Become an AI/ML Engineer - Do THIS Instead

Forbes has just listed AI Cloud engineer as one of the top 10 hottest AI jobs for 2025 and in between the numbers there's something hidden in front of us that nobody is talking about right now there's a massive shift happening in tech companies are desperately racing to build AI systems but they're hitting a major roadblock they can't find Engineers who understand both Ai and Cloud infrastructure but how desperate are they the numbers are staggering there's currently 5,000 open positions for AI Cloud Engineers yet only 890 monthly searches for this role for every person searching for

an AI Cloud engineering job there are five positions waiting to be filled but here is where it gets really interesting while everyone is fighting over the standard Ai and machine learning jobs where there are four people competing for every single position AI Cloud engineering roles are sitting empty so you know what does this mean this is the biggest opportunity in Tech right now the compet comption for AI Cloud engineering roles is incredibly low while the rewards are very lucrative with average salaries topping over $120,000 a year but it won't last forever as more people discover

this hidden gold mine the 5:1 ratio will disappear quickly so if you want to seize this golden opportunity and become an AI Cloud engineer where would you even start what does an AI Cloud engineer actually do and most importantly how do you position yourself to grab one of these rols before before everyone else catches on hi I'm slan I've spent over a decade in Tech and today I help companies scale and secure the cloud infrastructure on AWS and if there's one thing that's taken me years to learn it's this most people are even stuck in

certifications collecting mindset or they're in a tutorial hell never taking any decisive action and that's what most of my students tell me before they join my Academy and here is the advice that I give to them to truly stand out in today's job market you need to understand what businesses actually need and then position yourself as the person who can deliver it the opportunity right now is clear companies are sitting on mountains of data but there is a problem they lack Engineers who can turn all of this potential into real business value and this is

why AI Cloud Engineers are so valuable because they can build AI solutions that save hundreds of hours drive millions in revenue and create highly personalized experiences that keep their customers coming back I'm going to show you exactly how to become one of these highly paid Engineers using my proven full-face blueprint you'll discover the exact technical skills that companies are desperately hiring for I'll show you how to build your first AI Cloud project even if you have zero experience you'll learn which tools and Frameworks will put you ahead of 99% of our applicants and most importantly

you'll see how to position yourself to land these six figure roles so by the end of this video you won't just know what an AI Cloud engineer does you'll have a clear road map to becoming one of the most in demand Professionals in Tech today by the way you can join Over 11,000 Engineers accelerating their Cloud journey and grab my free Cloud engineering startup pack Linked In the description all right so most people fail to become engineers because they start in the wrong place they jump straight into AI Frameworks and complex cloud services missing the

foundation that makes everything else possible so in order to build AI Cloud projects you need to start at it and Cloud fundamentals specifically Linux the backbone of cloud computing nearly every AI system you build will run on Linux servers so you'll learn to use the command line and learn how to write simple scripts to automate repetitive tasks this naturally will lead into networking where you discover how these Linux servers communicate with each other using IP addresses and how to keep them secure with firewalls and vpns and as you're working with these systems you'll need somewhere

to store data and that's where databases come in you'll learn both SQL for structured data think of customer information or inventory and nosql databases for more flexible storage needs and finally you'll tie this all together with virtualization which lets you create multiple virtual servers from a single physical machine the technology that makes cloud computing possible in the first place and with these it fundamentals under your belt we have to choose a cloud platform and I recommend AWS because it's the market leader and offers the most job opportunities they also provide an extensive free tier perfect

for Learning and experimenting without having to worry about any of the costs now in AWS you need to understand five essential services that work together and don't worry we're going to implement AI in just a moment but for now covering ec2 which gives you the virtual service where you can run your applications these applications often need storage which is where S3 comes in think of it as an unlimited cloud storage for any files and folders or images and videos to keep everything secure you need identity and access management which lets you control who can access

what and you know we mentioned SQL and nosql databases earlier in AWS you'll need to learn the equivalence of RDS and Dynamo DB and finally VPC brings this all together by providing you your own private Network in the cloud where you can organize your resources exactly how you want now I know some of you might be worried about coding but you shouldn't be yes you do need to know how to write code but also you need to know what the lines of code that you write and review in codebases even mean so you can't skip

this step you have to learn it now this will take time and some experience but this is a journey rather than a destination now as an AI Cloud engineer you need to be comfortable with writing in Python and terapon these are your essential tools for Automation and infrastructure management now while you're learning these Services you should also get your AWS certifications especially if you're coming from a non-technical background you want to start with a cloud practitioner and then go into the AI practitioner then you want to move onto the solution architect SOA before going to

AWS security specialty this is a very nice and clean road map now I do also recommend you looking at the machine learning associate certification as a bonus but just remember these certificates should be seen as complimentary to your learning and not your main focus now I'm personally going through these certification myself so I only recommend to you guys what I do myself now before we get to building AI Cloud projects I need to give you some context on AI infrastructure what it is and how it's different to regular Cloud infrastructure now regular Cloud infrastructure ructure

is what powers most of the internet today when you use Netflix check your email or browse Instagram you're using regular Cloud infrastructure it runs on standard computers CPUs organized in data centers handling things like storing your photos processing payments or running websites but AI infrastructure is completely different while regular Cloud handles everyday task AI systems need much more power because they are processing massive amounts of data and doing all these complex math calcul ations at once think of analyzing millions of images or understanding human language tasks that need serious computing power and this is why

AI systems need specialized computers with powerful gpus now AWS provides these as P4 instances they're essentially high performance machines built for AI workloads and there is three key things that make AI infrastructure unique the first one is computing power which we just mentioned you need specialized GPU machines to handle AI task efficiently the second second one is network speed since AI moves huge amounts of data around the world you need extremely fast networks to keep everything running smoothly and finally storage AI works with massive data sets we're talking about terabytes or even petabytes this data

needs to be stored in data lakes and properly prepared before AI can even use it now as an AI Cloud engineer you'll design these systems manage a GPU infrastructure create secure data pipelines whilst keeping costs under control now that you understand the foundation let's get to building your first real AI project so for phase two we're going to start a simple small project that shows you how to piece everything together terraform AWS and AI now you might be wondering why we're using terraform instead of clicking around in the AWS console now as an engineer you

will never be clicking around the console and writing infrastructure because in the real world you need to be able to deploy and update your infrastructure reliably anyone that tells you otherwise has no idea what they're doing especially the ones on YouTube Imagine manually setting up all your AI systems by clicking buttons on the AWS console 3 months later when you need to recreate that same setup you won't be able to recreate it as there's no record of what you've done so please please please do not spend any time making infrastructure for a real project using

the AWS console I know there's loads of people on YouTube showing you the tutorials of how to build an ec2 and you know what it's fine to get familiar with the console but in the real world when you're working on projects you will not be clicking around and building infrastructure with with the console you'll be writing it as code specifically with infrastructures code so please please please don't watch any videos that show you how to use the ads console to build a cloud project it's fine initially so you get familiarity with that service but if

you want to build a real project for a portfolio you have to use IAC so for our AI Cloud project we'll spin up Cloud infrastructure piece by piece using terraform firstly we'll create VPC with subnets and an internet gateway to have access to the internet then we'll create an S3 bucket for storage and set up your IM roll with just enough permissions to make things work with Amazon bedrock and S3 Amazon Bedrock is aws's AI service now as everything is defined in code you can Version Control it and recreate it exactly the same way every

single time now for this project we'll work with some Simple Text data maybe a blog post or user feedback logs you'll store all of this data in S3 and use Amazon Bedrock to process it this data will be a knowledge base Bedrock will give you access to powerful pre-trained AI models through simple API calls think of it as sending a request and then getting AI response back no need to build an AI from scratch we use Bedrock to summarize the text or do some analysis now to bring it all together you need to write a

python script that handles this workflow uploading the text to S3 making API calls to bedrock for processing and then saving the results back to S3 and because this is a real world setup will Implement proper security controls encrypting the s3e bucket and using the least privileged am policy icies for all of our Bedrock calls now when you finish this project you have built your first AI powered pipeline in the cloud a production ready environment that connects infrastructure storage AI models and also your code and by the way if you want to start building projects today

then I do want to tell you that I'm running a special cohort right now and we're taking in new students for my cloud engineer Academy if you're ready to take action just like Jay Martinez who got laid off as a banker and got Cloud hired in just a few months or Mac who after joining my Academy landed a systems engineer role at AWS then click the link in the description and book a call with my team but just know that this isn't for everyone because it's not possible for us to bring on you know 100

people at once because the spots are limited and also the demand is really high right now and cloud is booming so we want to make sure we can actually help the people that we bring on so if you're interested go book a call and see if you qualify for our special AI Cloud cohort now before we dive into building Advanced AI Cloud projects you need to understand how modern AI systems are structured so there are fre connected layers that all work together to create an AI application that people actually use like chat GPT and understanding

each layer is important because they impact one another if one layer isn't working properly the whole system can break down so number one we have the infrastructure layer this is the foundation that everything runs on the specialized GPU servers high-speed networks and storage systems that we discussed earlier without solid infrastructure none of these AI capabilities above it can function properly number two the model layer this is where all the AI intelligence lifts the llms the large language models or even custom models built for specific business needs models need mlop systems to automatically maintain update them

as new data comes in they also use rag retrieval augmented generation to access company data making their responses more accurate and relevant number three the application layer this is what most people actually interact with like when you use chat gbt scrolling Netflix and you see their TV show recommendations now while as a user you never see the infrastructure or the models underneath these applications can only be as good as the layers supporting them and these layers are all dependent on each other for performance so if the infrastructure is slow the applications will be slow if

the models are maintained correctly the applications won't give you a good response that's why we need to understand and build each layer carefully now you've already set up a basic AI pipeline at the beginning of phase one connecting infrastructure storage and AI models it's time to take things further but automating the entire process ESS and this is where ml Ops machine learning operations comes in earlier we manually ran the workflow uploading data to S3 calling AI models through Amazon bedrock and saving the results but imagine your data is constantly updating like new customer feedback or

daily sales logs manually handling this every time would be a nightmare especially if the AI model needs retraining as patterns in the data starts to evolve with machine learning operations or mlops we can automate the entire life cycle so the new data arrives in S3 the pipeline will automatically kick things off then the data gets prepared the pipeline cleans transforms or formats the data as needed then the model is updated if necessary the pipeline will retrain the model using the new data then the updated version is tested and automatically rolled out this means you don't

have to manually manage updates it just works adapting to changes in real time now for this project we'll build on what you created in Phase 2 now you already have the VPC the IM rolls and S3 storage we'll expand on this by integrating a sage maker pipeline to handle the machine learning workflows and instead of only calling the pre-train models through bedrock you'll now trade and deploy your own simple AI model the pipeline will automatically trigger when new data is uploaded to S3 retraining and redeploying the model without any manual input with your infrastructure in

place and an automated mlops pipeline running we're now going to be making the AI smarter by connecting it to business data the next stage is all about rag retrieval augmented generation we built a pipeline using Amazon Bedrock to process text Data with pre-trained models and these models were powerful but they didn't have any knowledge of your business so you already have S3 set up as a storage system but now you use it to store your company's internal documents policies and manuals customer logs or any technical specification also the pipeline that we made earlier in phase

two you know the one that process text Data with Amazon Bedrock here you enhance it by adding a vector database using AWS open search to make the documents searchable by AI a vector database just means that your AI can now find and retrieve specific information from your data before using a model like Claude to craft answers and here's how we're going to build it in three simple steps firstly you want to start by taking your documents and turning them into embeddings by the way you can just generate documents in chat gbt for this part then

you want to load these embeddings into open search index where they'll be organized for efficient retrieval then you want to connect open search to your existing Bedrock based pipeline when a query comes in the system will search your data for Relevant documents pass the results to a language model to generate a accurate context aware response now so far your AI has been great at answering questions and handling tasks but what if it could work independently planning making decisions and executing complex workflows without constant oversight this is where AI Agents come into play AI agents combine

the intelligence of large language models with the ability to interact with tools access apis and take meaningful actions all automatically they go beyond just chat Bots and systems that can actually get things done in this phase you'll build an AI agent that automates the endtoend business workflow for example you could create an agent that pulls Market data analyzes it for anomalies and sends a slack alert with its findings building on your existing Pipeline with this agent will call apis to gather the information it needs using python and a connected llm it will process the data

to identify patterns anomalies and or trends like finding underperforming products or seasonal spikes it will then generate detailed reports upload them to S3 and then distribute them directly to the team members or it can notify them via email or slack to orchestrate this you can use Amazon Bedrock agents this enables your agent to work seamlessly across different tasks that said while these systems are incredibly powerful they must also be secure and reliable so you'll set up access controls and we use IM rolls to control exactly what each part of the system can and cannot do

setting specific permissions for who can access data or run certain processes everything your AI does gets tracked in cloudwatch this means if something goes wrong you can see exactly what happened and fix it quickly and the reason why this matters is because AI agents are the future of every industry and this project prepares you to work at the intersection of cloud engineering Ai and security and by combining AI intelligence workflow Automation and strong security practices you are creating a production ready solution that aligns with real business needs and drives real business value now phase three

is taking your new skills to the real world so here is is where I see the biggest opportunities and most importantly how to actually capture them every business is drowning in data but very few know how to use it and this is your leverage Point while other Engineers are focused on building complex AI systems the real value is in something simpler but more impactful helping businesses understand and use their data to create personalized experiences for their customers take an e-commerce website most sites still show the same products for everyone imagine Building A system that learns

from each customer's behavior and interactions and creates a completely personalized shopping experience or look at customer service small businesses are struggling with basic support tickets building an AI system that can handle common questions and root complex issues to the right people that's immediately saving the money and improving the customer satisfaction and the key is that you don't pitch the technology you pitch the business impact and the business value nobody cares about your rack systems your Cloud infrastructure or your machine learning pipelines instead tell them that you can help them understand why their customers are leaving

before let's say September and help them predict it for next year or predict which products will sell out for the next season or which products will be super popular for Black Friday maybe you can ultimate all their support to handle 80% of the tickets automatically now while everyone else is using generic AI models you can build specific systems tailored to business data and their needs and that's where I see the biggest opportunity right now is in customizing ation of businesses and their services and this is where the real value is in 2024 this is where

the biggest paychecks will come from and if you want to learn AWS then click this video right here to watch my brand new course that I've released which takes you from complete beginner to expert a toz and everything that you need to know