A Deep Dive into the World of AI Engineering: Is It Right for You? |

So, you're a student with a newfound interest in becoming an AI engineer, or perhaps you want to understand what an AI engineer actually does in a company. What's the difference between a software engineer and an AI engineer? And most importantly, should you even pursue a career in AI engineering? Is this field truly meant for you? Stick through this article, and you'll find the answers to all your questions.

If you need a short answer—though I suspect you might leave after hearing it, which could affect our readership, but that's okay—here it is: If you are not interested in deep, complex mathematics, this field is probably not for you. It's as simple as that.

Now, let's begin with the first question.

What Does an AI Engineer Actually Do?

After speaking with several data scientists and data analysts, it's become clear that in many companies, especially in India, the roles of an AI Engineer and a Data Scientist are remarkably similar. Their tasks often overlap, and they frequently handle each other's work. There isn't a massive difference between them.

So, if you were interested in data science, have built projects in it, and are skilled in that area, you can certainly apply for AI engineer roles as well. If you're familiar with the field, you probably already knew this. While the title "AI Engineer" sounds specific and suggests you're working exclusively on cutting-edge AI, the reality is that the work is not extraordinarily different from that of a data scientist. The work is excellent and challenging, but you can consider the AI engineer role to be quite similar to a data scientist's role.

A Glimpse into the Daily Work

So, what does the job typically entail? Consider an app you might use, like Zepto or any other delivery service. You've likely noticed that when you order milk, you automatically get recommendations for other items. Bananas, yogurt, bread, eggs, and other dairy products appear as suggestions. This happens on numerous websites too; if you buy one item, another is recommended as a good pairing.

These are called recommendation models.

Here’s how they generally work: 1. Human Intuition: Companies have a general understanding of human behavior. They know that if a customer buys a certain product, they might also be interested in another. This logic is integrated into their apps to show relevant products. 2. AI-Powered Insights: To go deeper, companies use AI models to analyze repetitive human behavior from years of collected data. The model observes patterns and provides appropriate, personalized suggestions.

This is just one example. There are countless other tasks that can be automated, accelerated, or simplified using AI. The goal is often to create something that reduces manual effort for engineers. In essence, a major part of an AI engineer's job is to integrate AI modules into pre-existing applications.

The Challenges on the Path to Becoming an AI Engineer

Now, let's talk about the problems, because there are quite a few. Can anyone just decide to become an AI engineer after a year of dedicated study? The answer is no.

The Scarcity of Entry-Level Roles

First and foremost, even if you take a great data science course, study diligently on your own, and put in all the hard work, it is very, very difficult to land an entry-level role as an AI Engineer or a Data Scientist. If you know someone who is a data scientist, it's highly unlikely that they landed that role right after graduating from campus as a fresher. This is a tough reality. Typically, you progress from other roles into a position like this. To get these roles, you need to be exceptionally skillful.

The Demanding Learning Curve

Next is the learning process itself. Many students enroll in data science courses and start with the basics, like Python. Basic programming isn't usually a hurdle for anyone; it seems easy. After that, they learn some fundamental tools like Excel, which are more common in data analytics but still necessary. People think, "Okay, I've done this, I understand the basic tools."

But then comes the actual theory.

Who becomes an excellent AI engineer or data scientist? The person with a profound theoretical understanding. If you ask anyone actually working in the field, they will tell you it's the people who have read numerous research papers.

Think about it: Do you read technical books? Have you ever taken an interest in a book on AI or a published research paper? Most likely, you haven't. This is why individuals who genuinely enjoy this process—often PhD students or those who love learning new things and diving into heavy mathematics—are the ones who excel.

The Crucial Role of Advanced Mathematics

This brings us to the most critical point: mathematics. We're not talking about basic geometry or the trigonometry you learned in 10th grade. The math required here includes: - Complex Linear Algebra - Advanced Calculus - Probability Theory - Combinatorics (to some extent)

It's the people who genuinely enjoy learning and applying these "messy" and difficult mathematical concepts who usually thrive in these fields. So, ask yourself honestly: Are you good at these things? Do you enjoy advanced mathematics? If you find joy in solving complex, even "boring," mathematical problems, then perhaps you are made for this.

The Reality of the Job Market

I also wanted to touch upon the nature of entry-level jobs. You'll often see videos, websites, and articles publishing numbers that suggest this industry is set to boom. They also highlight the vast number of job openings. However, there are a couple of catches here.

Industry Growth vs. Your Career: The first point is that just because an industry is booming and earning billions doesn't mean you will earn that money. There's no guarantee. It also doesn't necessarily mean there will be a positive impact on the availability of quality entry-level jobs.
The Nature of "Entry-Level AI Jobs": The second point is about the job openings themselves. Many of these so-called entry-level AI jobs are actually basic data engineering or data cleaning positions. These roles often involve more manual work than you'd expect. You aren't truly innovating in the field of AI. This is where the importance of reading research papers comes in—it allows you to build new logic or contribute to existing models. In most entry-level roles, you will barely get a chance to do any of that. You'll end up doing work that might not be very useful for your long-term career growth.

Is This Field Truly for You?

After considering all these points, if you still feel a strong passion for advanced mathematics and are genuinely interested in how AI works behind the scenes, then this field could be incredibly rewarding and lucrative for you.

It's important to distinguish this from simply using AI. A new role emerging today is the "AI Tools & ChatGPT Expert." These are not AI engineers; they are people who use AI to make their work more effective. That is a different role entirely.

But if you have a genuine interest in reading research papers and diving deep into the mechanics of AI, then yes, this field could be a fantastic fit for you, with the potential to earn a great deal.