The New Era of Software Engineering (in 2026)

If you want to become an AI engineer, then stick with me until the end of this video. I've worked in the industry for over 8 years at places such as Microsoft, and I've spent hundreds of hours learning how to use new AI tools to help streamline my development process. More and more companies are looking to hire AI engineers over software engineers. I'm going to go over a comprehensive step-by-step guide on what an AI engineer is and how to become one. And by the end of this video, you will have the exact knowledge to get ahead of other software engineers that are not putting in the effort to learning these new skills. Can I have a job, please? No. Get out of here. Scrant. Oh, no. So, first, what is AI engineering? This is a bit of a loaded question because it's changing quite frequently, but it's kind of like being a software engineer on steroids. There isn't one exact job description, but many people mistake AI engineers for the people that are creating the AI algorithms, not integrating them into software. This is not the AI engineering that I'm talking about. I'm referring to basically software engineers that know how to use AI tools and integrate them into their systems. It's actually a lot simpler than you may think. With AI engineering, you'll need to learn things like using hugging face and lang chain in your development flow, using cursor or augment to index your repo and assist you on enterprise project changes, understanding prompting in detail like prompt engineering, and even picking which LLM will be best to assist you for certain tasks. For example, if you wanted to add a hashmap to your code, you wouldn't just write the definition of a hashmap class from scratch. You'd use the Java standard library hashmap that's already defined for you. Likewise, if you wanted to use a certain LLM or algorithmic implementation, you wouldn't write that algorithm from scratch. You'd have to learn which AI integrations to use for whatever behavior you want. You're not reinventing the wheel by creating AI systems. You're using them within the context of software like a library or a tool. So, what does an AI engineer actually do in the real world? Think Alexa, chat bots, or self-driving cars. And these aren't just made by AI researchers. They're actually developed by real life AI engineers that are using AI models, APIs, and integrations to actually build these really cool products. Before AI came into the picture, software engineers might have used tools like this, Python or C as their language, frameworks like Spring Boot or.net, cloud resources like databases, web apps or message Q systems in Azure or AWS, and of course, Stack Overflow or just some old regular IDE. Now, as an AI engineer, all these tools still exist, but they're now supercharged by AI. This time, your IDE might have copilot built into it. You might use cursor or augment to index your repo, generate tests, and help navigate enterprise scale projects. This time, AI isn't replacing anyone. It's just amplifying your job. Okay, so let's look at an example. Let's say we're building an inventory tracking app. Instead of googling what's the best tech stack to use for my app, you can use a summarization LLM like chatbt, Gemini, or Copilot. When you start building, you'll have to pair your IDE with the right assistant. Maybe again, Copilot for Visual Studio, Augment for Visual Studio Code, or you can even go allin by using Cursor's IDE, which has built-in AI agents. These tools don't just autocomplete your code. They act like junior teammates. They install dependencies, debug, and refactor large code bases for you. That's what makes AI engineering so powerful. It's not about reinventing algorithms. It's about mastering how to work with AI systems. So, my recommendation is to pick two things. Your summarization buddy, that's chatubt or Gemini, and your IDE buddy, think copilot, augment, or cursor. These will be pretty important to use on the daily as an AI engineer. So, let's talk about AI integrations. As an AI engineer, it's not enough to just use AI tools to speed up your workflow, like cursor or augment. You also need to learn how to integrate AI solutions into your APIs. Think of it like this. Remember that example that I'd mentioned earlier? When you import a hashmap from the Java standard library, you're not reinventing the wheel by defining the hashmap yourself. You're using a pre-built optimized library. You're making things easier on yourself. It's the same exact thing with AI. Instead of training your own models from scratch, you'll plug in existing AI frameworks and API into your software. And this brings me to two words that you need to know. Hugging face and lang chain. Hugging face is basically the GitHub for AI models. It's pretty cool. I actually made a video on this a while ago, so check it out if you want a more in-depth analysis on how to build things with Hugging Face. Anyways, HuggingFace lets you find and integrate pre-trained models for text, image, audio, or even multimodule tasks. Think of it as a library for AI plugins. You can pull in models for things like text summarization or sentiment analysis, translation, document classification, image recognition, and these are literally just using a few lines of code in your APIs. Take a look. This is all you need to import a summarization LLM. That's it. You just integrated a worldclass AI model without ever touching any machine learning code. Lang chain on the other hand is how you connect all of these models in high-powered applications. It's kind of like the glue between your app and the LLMs. It gives you control over context memory and data flow, which is really nice. With Lang Chain, you can do things like create chat bots that remember past messages, connect LLMs to your own databases or APIs, and you can even build AI agents that perform reasoning or execute tasks autonomously. Pretty cool if you ask me. It's the perfect starting point for someone who wants to learn beyond just using chat GBT. And it actually gives you a chance to build AIdriven features. And of course, there are other AI integrations that you can play around with, too. There's open AI API. You can use models like GPT4 or 40 inside of your own apps. You can also use Anthropics Claude API or IBM's Granite or Watsonax.ai, Google Gemini API or the Coher or Mistral APIs. There's so many different options out there, but HuggingFace and Langchain are a really good starting point. So, all that being said, the next step in this road map is to get comfortable with integrating one or two LLMs into your APIs. Just start off easy with a simple hugging face model. They have a huge library to choose from. And then choose a couple more and string them together with Lang Chain. That's the next step. After you've successfully figured all of that out, then go explore some of the other integrations that I'd mentioned. And using Google Collab is the easiest way to get started. And don't worry, I'll put all of this information in the description below so you have the entire road map written out for you. Next up is prompting. LLM prompting is a must. Understanding how to talk and collaborate with an LLM is just as important as using an LLM. Hey, Chat PT, how's it going? I wanted to actually talk to you about something today. Gh, I know yesterday was crazy. Yeah, I'm still hung over, but anyways. Yeah, what's up with you? Okay. Yeah, not that kind of collaborating. As software engineers, we're used to giving very precise instructions. We give function arguments, API calls, HTTP requests, authentication, configuration files, everything as a software engineer has structure. Prompting an LLM is no different. It's not a magic box. The way you phrase your prompt determines how useful or useless the information you get back will be. So, think of it as setting up some sort of configuration that you have to give the prompt in order to give you a certain output. Another way I like to think about it is it's like you're pair programming with another developer. Maybe a junior or mid-level developer. If you give them vague directions, they'll make wrong assumptions and give you an incorrect output. This is fine. But if you give clear, structured context, they'll deliver exactly what you need. Here's how you can think about prompting. Be specific about the goal. So instead of saying write code for an API, you would have to get a lot more detailed than that. Say something like write a Python flask API with a login endpoint that authenticates users using JWT and connects to a Postgress SQL database. This is way more detailed and it also requires that you have some knowledge of building API infrastructure. So I think this is really important to note. You can't just have it vibe code for you. You have to actually understand the inner workings to ask good questions. You should also give the LLM context and constraints. LLMs perform a lot better when they know your environment. So, for example, you can prompt it something like, "I'm working in a Django project using Python 3.10. Generate a middleware that logs every request path and status code to Cloudatch." And then after all of that, iterate like you would in a code review. So, treat prompts as versioned instructions. Refine them step by step instead of expecting a perfect response on the go. Here's a pro tip. Start thinking about your prompts like an engineering toolkit, not just a chat input. You can save reusable prompts for writing unit tests, explaining complex functions, refactoring legacy code, and generating documentation. Soon you'll start building your own prompt library, just like if you save a collection of requests in Postman or Insomnia. It's kind of similar to that. So my next step for this road map would be to pick a summarization LLM similar to what you picked earlier and then practice prompting. Give it some bad prompts and then give it some good prompts to see the difference in the output and you'll be shocked by how much more accurate that information actually is. And of course, none of the above is possible if you don't know how to code in an enterprise environment. I won't get too much into the weeds with this, but all of the previous software engineering advice still applies. You can't just coast by vibe coding. I don't believe in that, at least not yet. You have to put in the work to actually learn the language. Understand how to use an IDE or a terminal. Even using cloud resources like AWS or Azure, those are all still really important. Again, AI is just amplifying the skills that you already have. And learning alongside the tools and integrations that I mentioned are the best way to get started. And if you're not familiar with basic software engineering concepts, I actually have a couple of videos that might be able to help you out. So, definitely check them out if you're a beginner to software engineering. Other than that, have a great day and happy learning. Don't forget that I've linked all of the tools that I mentioned in this video in the description below. So, definitely check that out as well. But happy learning.