How to Become an ai Engineer: Career Guide

You will hear many people asking about how to become an AI engineer. It is a valid and popular question. Artificial intelligence is now in everyone’s phones, banks, hospitals, and online shops. But someone must have built it, and that person is referred to as an AI engineer. 

This article will help you understand how to become an AI engineer in easy and understandable language. You will go through the real definition of the job, what the important skills are, and how you can become an AI engineer. 

What do you understand by an AI Engineer? 

An AI engineer is someone who builds software that can learn from data. Think of a music app that recommends songs you might like or a banking tool that flags unusual payments. An AI engineer is basically responsible for making these tools work. 

The following is what the daily routine in the life of an AI engineer looks like: 

• Gathering and organising the data
• Training Machine Learning models
• Testing if a model can perform well
• Putting the model in a real application

AI engineers operate their work with data scientists, software developers, and product teams. If you are willing to learn how to become an AI engineer, start by understanding that this job is a combination of maths, coding, and problem-solving. The better you understand this job, the better you can plan how to become an AI engineer. 

What is the reason behind choosing to be an AI engineer? 

One of the most famous reasons for choosing to become an AI engineer is demand. The U.S. Bureau of Labour Statistics (BLS) does not have an “AI engineer” listed as its own job. The closest group to an AI engineer is computer and information research scientists. The BLS has disclosed that this group achieved a median of $140,910 in May 2024. It shows significant job growth of 20% from 2024 to 2034, which is faster than the average rate. 

However, just know that these figures represent the data of the United States, and they cover a broader group that AI engineers do by themselves. The pay scale in India is different from other countries, which is why you must check local job pages to know the real numbers around you. 

Another reason to choose the path of becoming an AI engineer is variety. Understanding how to become an AI engineer makes you accessible to various paths such as health, finance, farming, games, and schools.

You can also read about how AI is changing the future of work in detail here:

What is the step-by-step process of how to become an AI engineer? 

Step 1: Build a base in mathematics

Math stands as the base for every AI model. However, you do not need to be a genius in maths. You can begin to learn it in small parts. 

The following are the topics that you should focus on: 

• Linear algebra: Linear algebra includes studying mathematical topics such as vectors and matrices that are responsible for holding the data. 
• Probability: Probability refers to the chances of something happening. In this part of the subject, you study how likely something is going to happen. 
• Statistics: Statistics is all about finding conclusive data. With these topics, you learn about reading, analysing, and comparing data.
• Calculus basics: One of the most important things to know about AI is that it changes almost every day. This is why calculus basics teach you how a model basically improves step by step. 

Step 2: Start with learning Python and SQL

One of the most common programming languages in this field is Python. It is the most beginner-friendly language, as it is easy to read and contains many free tools for data-related work. If you are someone who wants to know how to become an AI engineer, you should get your hands on Python first. 

The following are the most important parts of Python that you should focus on:

• Variables, loops, and functions
• Lists and dictionaries
• Libraries such as NumPy and Pandas
• Core and basic use of Git and GitHub

Step 3: Study Data Structures and Algorithms

Data structures are the ways that you can store data. Algorithms are the steps in the process of solving a problem. A good AI engineer would know both. Many recruiters try to test these skills, which is why it is important in the process of how to become an AI engineer. 

You can practise on sites such as LeetCode or HackerRank. Initially, go for easy questions and try for around 1 or 2 a day. 

Step 4: Study Machine Learning

Machine learning simply means teaching or training your computer to find patterns and learn from the data fed to it. Machine learning holds the biggest part in the journey of how to become an AI engineer. 

The following are the main types of machine learning:

• Supervised learning: Supervised learning provides data to the computer with already labelled answers. So that the model can learn from the data as well as the answers. 
• Unsupervised learning: The data that is provided to the computer under unsupervised learning does not contain any answers labelled to it. The model is supposed to find groups on its own. 
• Reinforcement learning: Lastly, Reinforcement learning is a bit different. Under this, if a model guesses the right answer, it receives a reward, and if it guesses the wrong one, it is given a penalty. This is how the system learns which answer is the accurate one. 

Step 5: Move forward with deep learning

Deep learning is an important part of machine learning. It uses neural networks that are created in layers, and they are somewhat inspired by the structure of a brain. The most common frameworks to learn in deep learning are PyTorch and TensorFlow.  

The following are the areas that you choose from based on what interests you:

• Natural Language Processing: NLP is all about training computers to work with human language. 
• Computer Vision: Computer Vision basically helps computers understand pictures and videos and analyse them. 
• Generative AI and large language models: This area of deep learning is basically responsible for writing texts or answering questions. 

Step 6: Start working on real and practical projects

Have you ever wondered why real-life projects are more important than a degree? It is because a degree marks that you have finished a course, whereas a project marks that you can use that course knowledge to solve a real problem. Employers look for individuals who can use their skills and knowledge to solve their brand-related problems.

The following are some of the simple project ideas:

• Creating a tool that can read reviews and suggest whether they are happy or sad
• Developing an application that differentiates between moon trees and skies in pictures.
• Building a chatbot that answers questions from a pile of documents. 

Step 7: Learn how you can make your models work

Just creating a model and letting it sit on your laptop will be of no use. When you put that model into the application that can be used by real people, it is known as 'deployment'.

The following are the basics that you must focus on and learn:

• Learning how to create a simple API with Flask and FastAPI.
• Understanding how to use Docker to package your code.
• Knowing the use of cloud platforms such as AWS, Google Cloud, or Azure.
• Verifying and checking a model after its launch.

What are skills that employers look for in an AI engineer?

The following is a quick glance at the important skills for anyone who is learning how to become an AI engineer: 

What are the technical skills that are important? 

• Python and SQL
• Machine learning and deep learning 
• Data cleaning
• PyTorch or TensorFlow
• Cloud tools and model deployment

What are the soft skills that are important? 

• Clear writing and speaking
• Teamwork
• Curiosity
• patience

Conclusion:

Now you know how to be an AI engineer, step by step. Let's start with maths and Python, along with machine learning and deep learning. Start with building real projects and deploy them. Learn how to deploy models. After you have completed your journey of understanding how to become an AI engineer, apply for jobs and keep on learning. The road is long, but the steps are small.

Anyone can learn to be an AI engineer with time and care. Pick one topic today and work on it for thirty minutes. That’s the real secret to being an AI engineer.