Segun Akinyemi

Christian, Software Engineer, Tech Enthusiast.

Be sure to connect with me on LinkedIn to continue the conversation about AI, tech careers, layoffs, anime (watch Steinsgate 🙌), Marvel (The Fantastic Four: First Steps 🔥), the new DCU (Superman 👏), Christianity, society, philosophy, you name it!

What’s Up

KakashiWaving

Welcome! If you’re reading this, you probably scanned a QR code during my workshop at SEO’s 2025 Tech Developer Conference in New York.

I posted this before the workshop, so I hope you’re like “that session was awesome” right now 😅. Or at the very least, I hope you enjoyed the fruit snacks if you got a swag bag.

Aside: Funables are the apex fruit snack. Don’t talk to me about Welch’s. I know you grew up with them, so did I, but they’re trash. Motts, trash. Annie’s, trash. Gushers, not fruit snacks. Black Forest, solid. Scooby Doo, mid.

To learn more about me, you can check out my about page or my Charlotte Business Journal 40 under 40 profile. Otherwise, let’s get down to business.

Quick Guide

During the workshop, I promised resources and answers. Here’s where to find them:

Core Concepts:

Resources:

Key Links:

What Is AI Engineering?

It’s still a relatively new term, although it’s going mainstream, but here’s my attempt at a definition.

AI Engineering is the practice of building software that connects AI models to real-world systems and knowledge. While Machine Learning Engineers create the models, AI Engineers make them useful by providing the right context, tools, and interfaces for specific applications.

Real Spill: AI Engineering is just the evolution of Software Engineering. Most AI Engineers today still have “Software Engineer” or “Software Developer” as their job title, and probably will for a while.

I created this visual to illustrate what AI Engineering means in practice. It’s not perfect, but it gets the point across:

AI Engineer Image

Aside: The image shows AI Engineer as the #1 fastest growing job title, which comes from LinkedIn’s 2025 Jobs on the Rise report.

Think of it this way. They’re both athletes, but in different sports.

Maybe Machine Learning Engineers are playing baseball while AI Engineers are playing basketball. Both benefit from being athletic (technical skills), but they’re different sports requiring different tactics, and one is harder (baseball is harder than basketball, don’t @ me).

Or maybe it’s more like Machine Learning Engineers build the engine and AI Engineers build the car around it, customizing for how different kinds of people plan to use it.

I don’t know, no analogy is perfect. The point is, Machine Learning Engineers build the models, and AI Engineers take those models and build with them.

The term was popularized by Latent Space.

I think it is a full time job. I think software engineering will spawn a new subdiscipline, specializing in applications of AI and wielding the emerging stack effectively…The emerging (and least cringe) version of this role seems to be: AI Engineer.

The Rise of the AI Engineer

And it’s got approval from Andrej Karpathy, former Tesla AI Director, OpenAI co-founder, and one of the most influential voices in the AI community.

In numbers, there’s probably going to be significantly more AI Engineers than there are ML engineers / LLM engineers. One can be quite successful in this role without ever training anything.

Andrej Karpathy on Twitter

Another term you might hear is Context Engineering, which is essentially the same thing.

Context Engineering is the discipline of designing and building dynamic systems that provide the right information and tools, in the right format, at the right time, to give an LLM everything it needs to accomplish a task.

Philipp Schmid of Google DeepMind

The terms are still evolving in this space, but both have caught on because they capture the same core idea. Making AI useful by giving it the right context.

It’s Easier, and That’s a Good Thing

At first glance, the term “AI Engineering” might sound intimidating, but let’s keep things real 💯. It’s way easier than being a Machine Learning Engineer.

To be a proper Machine Learning Engineer, you need to be good at math. Like, really good.

We’re talking calculus, linear algebra, statistics, probability theory, all that. And it’s customary for them to have PhDs. To command the kind of respect that researchers at OpenAI, Anthropic, Google, and Meta have right now, you’d need one.

Your path to being one of those people Mark Zuckerberg is throwing pro-athlete level contracts at is much longer and harder than your path to being an AI Engineer…where you can make a solid 6 figures at companies that already hire software developers.

I’m not alone in this sentiment:

In the near future, nobody will recommend starting in AI Engineering by reading Attention is All You Need, just like you do not start driving by reading the schematics for the Ford Model T.

The Rise of the AI Engineer

Aside: Attention is All You Need is the research paper by Google that introduced transformers, the “T” in GPT. It’s a Machine Learning hallmark that paved the way for what eventually became OpenAI’s ChatGPT.

Funny thing is, I actually told people to read that paper in my AI learning guide. But you know what? I was wrong. You don’t need to go that deep to get paid in this field. You can, and should if you’re truly that passionate about AI, but to have a tech career both now and in the future, it’s not a must.

For me, you, and anyone wanting a tech career who isn’t great at math or trying to get a PhD, our reaction to the rise of AI Engineers should be:

Gimli saying Aye I could do that

Why Coding Still Matters

You can’t build serious systems, the kind companies up and down the Fortune 500 actually want, if you don’t understand code.

Vibe coding in its truest sense (no code review, just the end result) is not a real thing for people with paying jobs. There are no shortcuts to mastery.

To all my “vibe coding will get me hired in tech” people out there, I love you, but…

Not Serious People

Aside: I haven’t watched Succession, and neither has Brian Cox, the actor in that gif. We’re both in agreement about avoiding good but stressful shows about deeply narcissistic, conceited, hedonistic people. I need at least one bastion of morality in my shows these days. Jon Snow, and Daenerys Targaryen before her tragic character assassination, are what got me through Game of Thrones. I need someone good, man. Someone.

Yes, AI can (and should) generate code for you, but if you have no idea what it’s doing, you’re flying blind. And no serious company is going to pay you to blindly copy-paste AI-generated code into their mission-critical (military, healthcare, infrastructure, financial, government, etc.) systems.

Sorry if that’s harsh, but I want to be sure I don’t have to go full…

Justin Bieber Not Clocking to You

…to get this point across.

So, are tech jobs going away? No! But they are for sure changing.

Sam Altman says the world wants 1000x more software. The CEO of GitHub says we need more programmers than ever. I’ve written extensively about this in my AI in tech careers post.

But the most compelling perspective comes from Andrew Ng. He co-founded Google Brain (they invented the “T” in ChatGPT), was Chief Scientist at Baidu, co-founded Coursera, and founded DeepLearning.AI. The entire Machine Learning community looks to him for training and guidance. His perspective on AI’s impact on programming carries serious weight:

Learning to code today isn’t about prepping for a future where you type every line yourself. We haven’t been doing that even before AI. High-level languages, Stack Overflow copy/paste, low-code tools, we’ve been doing anything and everything to make it easier to create software for a while now.

The future is being technical enough to orchestrate AI while you handle the problems that need human judgment.

Writing effective prompts, catching when AI hallucinates garbage, and debugging when things inevitably break. Those are the things no serious company will ever trust solely to a machine, and they’re what make you valuable and get you paid.

But you can only do these things if you can read and understand code, generate it with very specific prompts (essentially pseudocode), and occasionally, write it when needed.

What to Learn

If you ever thought you could be a Software Engineer (or Software Developer, literally the same thing, don’t let anyone tell you otherwise), then you can be an AI Engineer.

The same fundamental skill of coding is required, but you also need a solid understanding of the technical areas that AI Engineers (mostly still called Software Engineers) work with day-to-day.

Many of these are areas that real-world software engineers already deal with. Its the stuff beyond just coding that often surprises new grads when they start building production systems at scale.

Here’s what you’ll need to get familiar with:

Data Stuff

AI systems are only as good as the data they work with. To make data “AI-ready,” you need to organize it so AI can find answers quickly.

AI doesn’t understand text like we do. It converts everything into numbers called vectors (think of them as coordinates that capture meaning).

A vector database is like a search engine built for these numbers, letting AI find similar content even if the exact words don’t match. This is called semantic search, which means searching by meaning, not just keywords.

AI-Specific Technologies
System Architecture
Infrastructure & Operations

These aren’t things you need a PhD to learn. You don’t even really need a bachelor’s (although it certainly helps). You can learn all this stuff online.

Where to Learn

If you know basic Python, or any programming language, you’re ready to start. If not, begin with Google’s free Python course. After that, dive in.

Free Resources for Students
AI Engineering Courses

Check out my AI Engineering list on GitHub (some listings are repeated below).

Tools to Focus On

Stick with what most companies use. Don’t chase every hot new tool from a startup that’ll eventually be acquired by big tech (looking at you Windsurf, Replit, Inflection, and eventually, Cursor).

  • VS Code: The IDE everyone uses. Even Cursor is just a VS Code fork. The industry isn’t leaving VS Code. Microsoft’s vertical integration owning it and GitHub and Azure and a chunk of OpenAI is insane. Investing in being a VS Code power user is time well spent.
  • GitHub Copilot: Free for students. GitHub is owned by Microsoft, so it’s integrated everywhere. Specifically, it’s the preferred AI coding tool of VS Code.
  • Claude Code: Terminal-based AI-coding tool that works with any IDE (including VS Code). From Anthropic, it’s the real deal. A competitor to GitHub Copilot. Amazon is behind Anthropic, so this product has staying power.
  • GitHub Spark: GitHub’s answer to vibe coding tools like Lovable. Great for personal projects, micro apps, greenfield development, prototypes, etc.

Who to Follow

The tech community lives more on LinkedIn, Twitter (its real name), Reddit, and Hacker News than TikTok, Instagram, or Snapchat.

Tech people usually want to post and discuss without needing a picture or video to accompany it. Make a Twitter account just to follow these people if you need to.

The Top Tier
Organizations
Communities

What to Read

AI Basics
On Coding's Future
On AI Engineering
Critical Perspectives

Landing a Tech Job

The shortest and sweetest advice I can give is apply early and often. It took me 112 applications back in 2020, 3 of which resulted in interviews, and only 1 of which resulted in an offer, to get in with my current employer.

Some people might hear that and think:

Aint Nobody Got Time for That

But that’s what it takes. You’ve got to hustle, hard.

If you’re in college, literally when you arrive on campus in the Fall, it’s time to apply for internships. Set aside a few minutes every day, download LinkedIn to find positions, use Easy Apply, set up job alerts on Indeed.

If your goal is big tech, check the Microsoft, Google, Amazon, Meta, and Apple student career pages regularly. The big tech companies are always posting, and sometimes at different stages, so you really got to keep an eye on their websites.

I’ve written extensively about this in my AI in Tech Careers and Miscellaneous Tech Career Advice posts. But honestly, if you’re with SEO, you’re probably doing just fine with finding internships as they have great resources.

For those specifically interested in Microsoft
  • Microsoft Recruiters LinkedIn Group: This group isn’t discoverable through search, someone’s got to send you the link, and I just did. If you want a Microsoft internship, join this group NOW and keep an eye on it. Recruiters post opportunities here first.
  • Microsoft Careers: Main careers site, bookmark it.
  • Early in Profession: Your hub for internships and early career opportunities.
  • Explore Microsoft: Special internship program for college freshmen and sophomores.
  • Microsoft Hiring Tips: Microsoft specific tips on what they’re looking for.
  • Career Paths at Microsoft: Details on the many career paths, including many that aren’t technical and don’t require any coding or understanding of tech, at Microsoft.
  • Student Certifications: Microsoft lets students take certification exams for free. Good stuff to throw on your resume.
  • Student Ambassadors: Become a Microsoft rep on your campus.
  • AI Skills Navigator: A tool that uses AI to help you learn AI that Microsoft is pushing hard right now.

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