The Ultimate Guide to Becoming an AI Engineer

Just a few years ago, Artificial Intelligence (AI) seemed like a futuristic idea that was far, far away.

Today, it has already been integrated into our everyday lives, across manufacturing, IT, healthcare, and many other sectors, creating a huge demand for AI engineers.

What Does an AI Engineer Do?

An AI engineer implements AI models into software systems to automate problem-solving and intelligent decision-making, delivering a smoother user experience and more.

The key responsibilities of an AI engineer typically include:

• Developing and training custom machine learning/deep learning models.
• Deploying the ML/DL models to cloud platforms and setting up the APIs correctly.
• Ensuring the ML/DL models are reliable and scalable.
• Integrating the ML/DL models into the software system via APIs.
• Collaborating with software engineers, data scientists, and other members on the team.

Should You Pursue a Career in AI

AI is not just an independent sector in the tech industry. It is the catalyst in almost every field that makes things run more smoothly and efficiently. This is why it is in such high demand across every industry.

As of 2025, the median salary of an AI engineer is $184,498, and the number goes up to around $250,000 in major cities such as New York and San Francisco, making it one of the most well-paid career paths in the tech industry.

So if you are passionate about cutting-edge technology, have a strong interest in programming and mathematics, and, of course, getting paid generously while doing what you love, then you should definitely consider an AI engineer as your future career path.

If you're interested, this article lays out a clear roadmap for you.

Necessary Skills of an AI Engineer

Becoming an AI engineer requires a rather strong technical background. You should have a deep understanding of programming (Python), mathematics, data structures and algorithms, as well as AI models.

Python Programming

Python is the go-to language for AI. Its readability allows engineers to focus on problem-solving rather than cryptic syntax.

Python also comes with a huge collection of tools and libraries such as NumPy, Pandas, Scikit-learn, TensorFlow, and PyTorch, which are essential for AI development.

The language also integrates easily with web frameworks such as Flask and FastAPI, allowing engineers to quickly set up APIs for their AI models.

Mathematics

As an AI engineer, you need to understand how things work, not just how to implement a solution.

Therefore, it is important that you understand the math behind the AI models to become a good AI engineer. Here are some of the key topics you should master:

Linear Algebra

Linear algebra deals with vectors, matrix calculations, and linear transformations. It allows us to express data and perform transformations in a structured manner.

Matrix calculation is also the mathematical foundation behind neural networks. Each layer of the network is just matrix multiplication.

Calculus

Calculus studies how things change over time using derivatives and integrals. It is one of the most important mathematical foundations for AI, and is especially useful for optimizing AI models.

Probability Theory

AI is all about making predictions using existing data, but real-world data is always noisy and filled with uncertainty.

Probability theory allows AI to deal with that uncertainty mathematically, allowing machines to give more accurate predictions.

Data Structure and Algorithms

Data structure and algorithm (DSA) is another crucial topic you should understand.

DSA is about how to organize data and how to process it as efficiently as possible. And choosing the right DSA can significantly impact the performance of your AI model.

Some key topics you should focus on are:

• Arrays & Lists
• Stacks & Queues
• Trees
• Graphs
• Heaps and Priority Queues
• Sorting & Searching Algorithms
• Graph Algorithms
• Dynamic Programming
• Greedy Algorithms

Unless you have a strong passion for mathematics and DSA, they are going to feel difficult and boring.

But the good news is, although these theoretical concepts are crucial for AI engineering, they are not a prerequisite for you to get started.

It is completely OK for you to jump over to the more practical part of AI engineering and start doing real projects. And then come back to study the theories when you encounter problems you cannot solve.

AI Frameworks & Tools

AI frameworks and libraries help AI engineers accelerate the development process. It is important to master these tools for aspiring AI engineers.

Some common frameworks and libraries include:

NumPy

NumPy is a Python library used for numeric calculations. It supports vector and matrix calculation, which are very important for linear algebra.

Pandas

Pandas is a data analysis library for Python. It supports advanced data structures such as series (one-dimensional labelled array) and DataFrame (two-dimensional labelled array), as well as data manipulation utilities for data reading and writing, indexing, cleaning, and processing.

Scikit-learn

Scikit-learn is another Python library designed specifically for machine learning. It provides prebuilt algorithms, preprocessing utilities, and metrics that help you evaluate the AI model.

Hugging Face

Hugging Face is an AI community offering a huge collection of AI models, tools, and datasets. It is the GitHub for AI engineers, allowing you to quickly build applications with the pretrained models and deploy via the Hugging Face Hub and its API.

MLflow

MLflow is a platform that enables users to manage the entire machine learning lifecycle. It tracks the key metrics, organizes models, and streamlines the deployment process, offering a unified platform for all your machine learning needs.

OpenCV

OpenCV is the go-to library to use when it comes to computer vision and image processing. It is very important if you lean towards self-driving algorithms, facial recognition, and any other AI branches where image processing is an important component.

TensorFlow

TensorFlow is an open-source machine learning framework created by Google. It allows you to develop machine learning models quickly without having to worry about complex computations.

PyTorch

PyTorch is another machine learning framework developed by Meta. It is more beginner-friendly as it supports native Python syntax, while offering similar features and performance.

PyTorch is very popular among researchers and developers, and TensorFlow is the choice for most businesses and enterprises.

Machine Learning

Machine learning involves a set of algorithms that derive patterns from data and then give predictions accordingly.

Some of these models evolve over time, meaning their predictions are going to be more and more accurate every time.

These machine learning models form the foundation of AI.

Supervised vs. Unsupervised Learning

Based on the training method, machine learning models can be divided into two categories: supervised and unsupervised.

Supervised learning is when the model is trained using labelled data, meaning for every data input, there will be a correct answer attached, which is referred to as the label.

One example of this is a spam filter. You will feed the AI model with thousands of emails, which are labelled either spam or not spam.

The AI model will then try to identify patterns in the email, which will then be used to predict if new emails are spam or not.

Unsupervised learning, on the other hand, is when the AI model is expected to find hidden patterns on its own.

For example, you feed the AI model thousands of user profiles, with information such as:

• Age
• Job title
• Income
• Purchase habits

But without providing a label.

The AI model is expected to discover a pattern on its own, perhaps:

• People with higher incomes buy more expensive products.
• People with lower income buy cheaper products and take longer to make a decision.

Supervised learning is commonly used for value prediction and classification, while unsupervised learning is usually used for pattern discovery.

Semi-Supervised Learning

Semi-supervised learning is a mixture of both supervised and unsupervised learning.

It is when you give the AI model a small set of labelled data for initial training, and then feed a larger amount of unlabeled data for self-training.

This is because labelling data is usually a time-consuming and costly task.

Semi-supervised learning allows the AI model to complete an initial training with a small dataset with correct answers, and then refine its understanding with more unlabeled data to provide more accurate responses.

This is very close to how we learn new things in the real world.

Machine Learning Algorithms

Under the umbrella of machine learning, there are several types of algorithms that an AI engineer must understand.

Some of them fall under supervised learning, including regression and classification algorithms, while others fall under unsupervised learning, such as clustering, dimension reduction, and association algorithms.

Many of these can also be modified to become semi-supervised learning.

Regression Algorithms

Regression algorithms are used to predict numeric values based on continuous input data.

It can be used to predict the future price of a product based on historical data, estimate marketing trends based on past stock prices, predict possible sales numbers according to company spending, and so on.

Some common examples of regression algorithms are:

• Linear & Polynomial Regression: Linear regression identifies a straight line relation between input and output. Polynomial regression identifies a high-degree polynomial relation that is non-linear.
• Ridge & Lasso Regression: Both are improved versions of linear regression, by adding a penalty term in order to avoid overfitting.
• Decision Tree Regression: Divide the dataset into smaller portions based on different features.
• Random Forest Regression: Merges multiple decision trees together, and averages their result for improved accuracy and robustness.
• Support Vector Regression: An alternative version of Support Vector Machine (explained below) designed specifically for regression tasks.
• Neural Network Regression: Neural network designed for regression tasks, perfect for learning complex, non-linear relations between datasets.

Classification Algorithms

However, not everything is continuous in the real world. Sometimes

Classification algorithms are used to predict discrete categories, such as

• If an email is spam or not spam
• If an image is a cat or a dog
• If a paragraph has positive, negative, or neutral sentiment.

Some common examples are:

• Logistic Regression: Don't be fooled by the name, it is used for classification. It uses a sigmoid function, which outputs a number between 0 and 1, to give the probability that the input data belongs to a certain category.
• K-Nearest Neighbours: Classifies the new data point based on the category of its K nearest neighbours.
• Decision Tree Classifier: Splitting the dataset repeatedly based on different features, creating a tree-like structure.
• Random Forest Classifier: Merges multiple decision trees and takes their averages to achieve higher accuracy.
• Support Vector Machine: It attempts to locate the best boundary that separates the dataset into different groups.
• Naive Bayes Classifier: Created based on Bayes' Theorem. Best for textual information.
• Neural Networks: Neural networks designed for classification tasks.

Clustering Algorithms

Clustering algorithms are very similar to classification algorithms, except that they are unsupervised. Instead of telling the AI model what categories the data should be classified into, the model is expected to split them into appropriate groups on its own.

This is useful when you have a large amount of data, but you don't know how to categorize it.

Some common algorithms are:

• K-Means Clustering: Works by identifying a random K center points, and then assigning each data point to its nearest point to form different clusters. Calculate the mean of each cluster. Then repeat the process until the clusters reach the optimal state.
• Hierarchical Clustering: Operates either top-down or bottom-up. Top-down starts with a single cluster and splits recursively to form the desired number of clusters. Bottom-up starts with individual data points and then merges them into larger clusters.
• Density-Based Spatial Clustering: Split data based on density. Marks data that are packed together as in the cluster, and marks data that are sparse as outliers.

Dimensionality Reduction Algorithms

Machine learning is all about extracting features and parameters from datasets and trying to identify patterns and structures.

However, in practice, many real-world datasets are too complicated to be analyzed easily.

For example, imagine you are trying to use AI to analyze the stock market. Each stock would have hundreds of features such as the 5-day average, 10-day average, interest rate, inflation, and so on.

Not to mention, these features are often correlated. This is what's called a high-dimensional dataset.

Trying to process it without any simplification will waste a lot of time and resources.

This is where dimensionality reduction comes in. It helps you convert a complex, high-dimensional dataset into a simple dataset with just a few principal components. Each component would contain a group of features that are correlated.

Some examples are:

• Principal Component Analysis: A linear dimensionality reduction technique by mapping data into a new coordinate system.
• t-SNE: A non-linear technique for visualizing high-dimensional data in 2D or 3D.
• UMAP: An alternative to t-SNE that is faster.

Deep Learning

Deep learning is a subset of machine learning that is composed of multiple layers (hence the name "deep") of neural networks.

Unlike other machine learning models, which require manually selecting the correct features, deep learning models can automatically learn and identify the most relevant features directly from raw data.

For example, imagine you are trying to identify the different animals from a collection of photos.

When using the traditional machine learning model, you have to manually extract the appropriate data, such as the edges, shapes, or colours, and then feed the processed data to the model.

However, when using a deep learning neural network, it can directly process raw images and identify useful features automatically through multiple layers of abstraction.

The outer layers detect simple features such as the edges, and the deeper layers identify complex features such as shapes and species.

Mastering deep learning is crucial for AI engineers, as it is at the core of modern AI systems, especially in areas such as computer vision, natural language processing, and speech recognition.

Neural networks come in different types, each designed to tackle a specific type of problem. Here are some of the common neural networks in the field:

Artificial Neural Networks (ANN)

An artificial neural network is the most fundamental type of neural network. The data flows in one direction in this case, from the input, through many layers of neural networks, and then returns an output.

It works best with structured data and is often used for prediction tasks, such as predicting if a phone call is fraudulent or if an email is spam.

Convolutional Neural Networks (CNN)

Convolutional neural networks are designed to process visual information, where each neuron focuses on a small part of the image, allowing the entire network to have a sense of the spatial hierarchy.

It is best for identifying objects commonly used by self-driving cars and surveillance systems.

Recurrent Neural Networks (RNN)

A recurrent neural network is just like an artificial neural network, with one crucial difference: RNNs remember past information.

It is commonly used for tasks where the sequence of data matters, such as predicting the future price of a stock, given its historical prices in the past 30 days.

Model Deployment

Besides developing and training the AI models, it is also the AI engineer's responsibility to deploy the model to a cloud platform and ensure it is accessible, scalable, and performs as expected.

Some tools that AI engineers are expected to know are:

• Flask & FastAPI: Frameworks for creating APIs and web apps.
• Docker: Creates containers that isolate apps and their dependencies.
• Kubernetes: An orchestration system for applications built with Docker.

And of course, an AI engineer should also have experience with at least one of these cloud platforms:

• Amazon Web Services (AWS)
• Google Cloud Platform (GCP)
• Microsoft Azure
• Hugging Face Hub

Choosing a Specialization

Besides what we discussed above, there are some specialized skills you may need to master, depending on the specific career specialization you pursue.

For example, if you are interested in self-driving cars, facial recognition, or defect detection, then you will need to be familiar with the following technologies:

• Image Processing: Know how to use libraries such as OpenCV.
• Object Detection: Know how to use libraries such as You Only Look Once (YOLO), which identifies multiple objects in an image.
• Convolutional Neural Networks: This is one of the most important AI models for processing images.
• Vision Transformers: A subset of transformer algorithms that works on images.

Natural language processing is another exciting specialization for AI engineers. If you lean towards chatbots, document processing, or sentiment analysis, then focus on:

• Text Processing: Need to understand text processing related skills such as tokenization, stemming, lemmatization, and so on.
• Word Embeddings: Transform words into numbers for further processing.

If you are pursuing a career in voice assistance or speech detection, then you'll need skills related to:

• Signal Processing: Extracting meaningful parameters such as frequency, pitch, and amplitude from voice signals.
• Speech Recognition: Turn human voice into text.
• Speech Synthesis: Turn text into human voice.

Reinforcement Learning

Reinforcement learning is when the AI model interacts with the environment and then receives either rewards or penalties.

The model will then use that feedback to give better responses next time, allowing the AI model to learn and grow based on trial and error.

This is also a technique that every AI engineer should know, regardless of specializations.

Reinforcement learning is very useful for robotics, autonomous systems, self-driving cars, game AIs, and any other field where you want your AI to be "smarter" over time.

Here are some of the most fundamental concepts in reinforcement learning that an AI engineer should know:

• Agent: The party that makes the decision. This could be a self-driving car, a robot, or a delivery drone.
• Environment: The environment that the agent must interact with.
• State: The current situation of the environment.
• Action: The decision that the agent makes according to the current state of the environment.
• Reward: The feedback from the environment that the agent receives after taking the action.
• Policy: The rules that the agent must follow when taking actions.

In practice, an AI engineer will take these fundamental concepts and form a mathematical formula that models real-world situations.

Generative AI

Generative AI is an advanced form of AI that generates new content based on existing data, rather than just making predictions. Some examples are ChatGPT, Claude, and DeepSeek.

As more and more companies start to integrate generative AI into their systems, it is important for AI engineers to understand how they work.

Recommended Roadmap for an AI Engineer

As you can see, there's a reason that AI engineers are so well paid, it requires a broader skill set and a deeper understanding of real engineering skills compared to web development.

So don't try to rush the process when you are learning to become an AI engineer, mastery takes time.

Here we have prepared a structured roadmap that will help you excel as an AI engineer.

Practice Projects You Should Build to Become an AI Engineer

Building actual projects is always the best way to learn new technologies. It not only improves your skills but also enriches your resume.

This section lists some real projects you could try, covering the beginner to the advanced level.

And to give you an idea of how to approach these projects, most of them should follow these steps:

• Prepare the dataset: In most cases, you can find publicly available datasets. If not, you can scrape the internet for raw data.
• Clean the data: Preprocess the dataset for training, remove anything else that would affect the accuracy of the model.
• Extract features: Extract the appropriate features for the model to train, such as the edges and colours of the image, word embeddings from text, and so on. However, if you are using a neural network, this step can be skipped, as it works with raw data directly.
• Train the model: Choose the appropriate model based on the type of tasks, whether it is classification or regression, or if it's image-related, text-related, or voice-signal-related.
• Model evaluation: Evaluate the accuracy of the model by testing it on a new dataset.
• Deploy: Set up the correct API and deploy the model.

Spam Email Detection

This is one of the best beginner-level projects that an aspiring AI engineer should try.

The purpose of this project is to build a classification model that marks an email as either spam or not spam.

You can train the model using the public datasets from SpamAssassin.

Post Sentiment Analysis

This project is to build a model that detects the sentiment of a given social media post, whether it is positive, negative, or neutral.

It follows the same approach, except you need to use a different dataset, such as Twitter Sentiment140, IMDB Reviews, or Amazon Product Reviews.

Stock Price Prediction

This is a typical regression problem that requires you to create a regression model that predicts the future price based on historical prices.

The training dataset can be sourced from Yahoo Finance, Alpha Vantage, or Quandl APIs.

Handwriting Detection

This is a classifier project that requires you to have some image processing skills. The goal is to map handwritten digits to actual letters.

You may acquire the training datasets from MNIST or EMNIST.

Real-Time Object Detection

Real-time object detection is an advanced AI project, and also one of the most exciting ones. The goal is to create a model that identifies multiple objects in the image in real-time.

This is the core technology behind self-driving cars, surveillance systems, facial recognition, and many other branches of the AI industry. It will also be a great addition to your resume to demonstrate your skills.

Voice Command Detection

This project aims to create a model that accepts a voice signal, recognizes the command, and performs the corresponding action according to the command.

This is the foundation behind voice assistance such as Alexa, Siri, and Google Assistant.

AI Engineer Learning Resources

The life of an AI engineer involves continuous learning and evolving. To help you stay updated on the future development of AI, we have organized a list of learning resources that will help you grow as an AI engineer.

AI for Everyone from DeepLearning.AI

AI for Everyone is a beginner-level introductory course, designed to explain core AI concepts to non-technical people.

The course answers some of the most commonly asked questions in the field, such as what is machine learning, what deep learning does, how to integrate AI into your business, and so on. All explained in simple, non-technical terms.

This 6-hour course is a great place to start your AI engineer journey.

AI Engineer Roadmap by freeCodeCamp

AI Engineer Roadmap is a YouTube video made by freeCodeCamp. It introduces some of the key concepts in AI, covering mathematical foundations, data science skills, traditional machine learning, deep learning, generative AI, LLM, and so on.

This course is more technical compared to the AI for Everyone course, so make sure you have some mathematical and programming skills.

IBM AI Engineering Certificate on Coursera

IBM AI Engineering Certificate is an intermediate-level course covering machine learning with Python, PyTorch, neural networks, generative AI, LLMs, and so on. This course takes a more practical approach, and is designed to get you job-ready within 4 months.

Reinforcement Learning Specialization on Coursera

This Reinforcement Learning Specialization course from the University of Alberta delves deep into the concept of reinforcement learning. It discusses how to implement a reinforcement learning system, covers several reinforcement algorithms, and helps you understand how it works with other AI learning methods, such as deep learning, supervised, and unsupervised learning.

AI Training Courses from edX

edX features a collection of different AI courses from different universities and companies, covering essential math in AI, AI for marketing, AI in web-based apps, and so on.

With over 500 different courses, you will definitely find one that works for you.

Frequently Asked Questions

Can I become an AI engineer without a degree?

Yes, absolutely, in fact, many AI engineers are self-taught, and some of them even come from non-technical backgrounds. Employers value experience more than a degree. You should focus on building a good portfolio that demonstrates your skills in Python, machine learning, deep learning, and any other related technologies.

Should I choose TensorFlow or PyTorch?

Both of them are popular machine learning frameworks. PyTorch is very popular among researchers and developers, and TensorFlow is the choice for most businesses and enterprises.

We recommend starting with PyTorch as it integrates natively with Python, and is easier to get started, and then gradually ease into TensorFlow, which is more robust for enterprise systems.

Do I have to master math and DSA before start learning AI?

No, they are important skills for an AI engineer, but you don't have to start with them. The best approach is to learn these concepts in parallel as you study AI. Start building actual projects, and then come back to these theories when you encounter real problems.

What salary can I expect as an entry-level AI engineer?

In the US, entry-level AI engineers typically earn about $90,000 to $120,000, depending on the company, specialization, and their skill set.

How long does it take to become an AI engineer if I'm studying part-time?

By dedicating 10 to 15 hours per week, you could reach the entry-level AI engineer in around 10 months.