In this article we are going to show you a road map which will help you to guide to land a high-pay tech job working in artificial intelligence (AI). There’s only one thing that stands between you and working in this exciting field. You’ve already seen that AI is the new gold rush things are heating up and AI is going to bulldoze his way across every industry. The best place to be right now is at the Forefront of this once in a-lifetime Trend and there’s only one way to get there.

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We know you’re unhappy with your current job and you can sense that now is the time to upgrade your career. If you focus on this one simple thing onallinone will be chasing you down with job offers. But if you ignore it you’ll look back wondering what could have been if you would have just done this one thing.

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• AI Engineers are making $150,000/Annual Or $72.48/Hr (Based On 847 Salaries)
• Data scientists 130,000/Annual Or $62.40/Hr (Based On 10000 Salaries)
• The starting salary of a data analyst is impressive at $57,000/Annual Or $37.81/Hr (Based On 10000 Salaries)

These guys are working for some of the most exciting Innovative companies in Tech right now because they chose to do this One Thing.

Before we explain it to you let’s just make sure we’re on the same page about what AI really is so when we say AI we are not talking about using chat GPT at your job this isn’t about prompt engineering we’re talking about AI as a field within computer science.

• You are smart enough to know that when your wife says I’m fine, she is not fine-well we are talking about the kind of AI that has the ability to understand that as well.
• This AI has the ability to think and make decisions to understand language to rationalize like you and we do.
• This AI is created using machine learning (ML) which is a discipline that focuses on computers getting smarter without us having to to give them explicit instructions.
• the next layer down is deep learning(DL) which somewhat mimics how the human brain functions this is a subset of machine learning that gave us chat GPT.

Now where you want to be is in data science which is a field that covers all these disciplines and more so if that’s correct then let’s talk about the One Thing that you need because a career in AI is possible. There is a pathway for you but you need to focus on doing this One Thing.

One Thing:- Stop Making Excuses

Anyone who’s ever achieved anything in life got to a point where they decided that nothing was going to stand in their way and that tech company with that killer job and data science is waiting for you.

They need you to make the same decision in your own life you see there’s a job shortage for quality individuals to work within these roles don’t listen to people talking about how they can’t get an interview or the job market isn’t great or they can’t do it because it’s a male dominated industry.

Basic Foundation Before Working In AI

• You’re going to need a solid foundation in basic math’s you don’t need to be an expert mathematician but you do need a basic understanding of linear algebra and calculus.
• AI is built on top of Concepts from linear algebra such as matrices and linear Transformations Concepts from statistics such as statistical significance, distribution, regression and likelihood will help you interpret data.
• You’re going to learn to program in Python and R
• You’ll have to understand data structures that allow you to store, retrieve and manipulate data and of course data manipulation which involves cleaning and transforming data so they can be analyzed or fed into AI models.
• More broadly you’ll need to learn data science which blends tools, algorithms and machine learning principles to discover patterns in raw data for making predictions you’ll learn machine learning and deep learning for using neural networks.

There are a lot of Concepts there to master, equally important are the tools and packages that you’ll need to learn as well. If this scares you then just click away now but if you’re excited and motivated by The Challenge then let’s move on to the 10-month road map to help you break into Ai and data science.

Months 1 To 3

• You’re may focus on the math’s-learning programming data structures and manipulation studying algebra, calculus, statistics and probability will give you a strong Foundation to succeed with AI.
• Learn a program in Python and start learning about data manipulation and Analysis learn how to clean and prepare data which is fundamental for machine learning or artificial intelligence.

Months 4 To 6

• Things are going to start to heat up as you dive into the basics of AI and the various branches of this science.
• You’ll also go deeper into machine learning to learn about model types, model validation and hyper parameter tuning.

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Month 7 To 9

• You’re nearly there you’ll dive into neuron networks and deep learning you’ll also learn machine learning Ops which is applying dev. ops principles to machine Learning Systems.
• Finally you’ll get started in an interest area this could be natural language processing computer, vision reinforcement learning or some other field.

Month 10

• You’ve reached the end of the road map but your learning doesn’t stop there depending on your particular path you’ll need to deepen your understanding especially to land a job as an AI engineer.

Now you’ve got the base level Knowledge and Skills you’re going to need to get job ready. Finding a job is never straightforward just because you took the training and acquired the skills doesn’t mean you’re entitled to a job you need to prove that you’re a good hire.

How do you prove that you’re worth hiring, well you can use this four-step method for building a killer portfolio for standing out and getting hired in an AI job.

Step 01:- Do Free Work

• finding a paid internship where you can get experience and direction from more senior experts should be your first option but internships aren’t always so easy to come by so what should you do, work for free.
• Volunteer to create something for your local church, reach out to a local business and tell them that you can use machine learning and data science to help them predict customer turn, sell them on how you’re able to predict customer demand.
• Get creative you’ve just learned a whole new bag of tricks convince them to let you work on a project for free.

Step 02:- Drive Results

• Take on a project that you’re confident that you can complete and will create significant business value.
• This step is crucial it’s not just enough to work on a cool project you need to show that you worked on a project that provided real life tangible business value.

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Step 03:- Document The Process

• Create a website, a Blog- publish what you did, show everyone how you created value for that business or that organization.

Step 04:- Get A Customer Testimonial

• If you can get something on video it’s much more powerful than a written testimonial.
• We guarantee-if you get the CEO of even a small business to come on camera and talk about how your machine learning algorithm either saved them money, made them money or prevented them from losing money you will have changed the equation you will stand out and you will be in demand.
• In parallel to building your portfolio you need to be expanding your network connecting with the right people and showcasing your talents is the way that you land a job in AI.

So you’ve put in the work you landed a job in data science or AI now it’s time to talk about!

Advancing Into The World Of Artificial Intelligence Engineering

 This gig is the secret sauce behind all the AI Magic.

• Picture this you’re the architect, the one who crafts the code that breathes life into AI. Making it think and learn like a human brain. You’ll blend together skills of software development, programming, data science and data engineering.
• AI engineering isn’t exactly the same as data engineering where experts are building like data highways.
• AI Engineers are like data detectives you’ll hunt down just the right data from various sources and that’s just the beginning once you’ve got the data it’s time to get creative.
• You’ll create and fine-tune machine learning models these models are the powerhouses that run AI applications.

Here’s a Twist you won’t just be writing lines of code you’ll be making API calls you’ll be embedding code and you’ll be giving AI a real world presence.

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Conclusion

Remember, this isn’t your typical non coding job that you’re shaping the future. AI engineering is where Innovation takes flight and you’re the pilot. If you’re new to AI or just getting started in data science you’ll first want to advance your skills in software engineering and programming.

You’ll also need to advance your skills in data science and data engineering before you really dive into the world of AI engineering. If you’ve got some experience already you should check out the learning resources from Microsoft on AI engineering

So as you can see AI is a challenging and rewarding care career choice, however if you decided that you want to move into Tech but AI isn’t the right pathway for you then you’ll want to seriously read this article and start to apply an ideas on your daily life.