AI Engineers are in demand in most industries, and there’s a good reason for this. If you’re wondering what an AI Engineer does, we’ll break it down for you.

Businesses can use the massive amounts of data they generate daily to improve and simplify common, everyday tasks. With the right AI systems, companies can take these tasks off the hands of their teams so they can focus on more meaningful work. Technologies like generative AI, speech recognition, business process management, and image processing are only some of the AI technologies changing the world.

In this article, we’ll explore what AI Engineers do, what kind of skills they need, and how you can get started on the AI engineering career path.

But first, let’s examine what AI engineering is and how it relates to machine learning.

What is artificial intelligence?

AI, or artificial intelligence, uses computers and machines to emulate how the human mind operates to accomplish problem-solving and decision-making tasks. It combines the robust data sets we generate daily with computer science to achieve this goal in its simplest form.

In AI, machines learn the outcomes of specific actions by crunching mountains of past data. They then use the insights gained from this process to make decisions about future actions and solve problems. At the same time, data is collected on the machine’s decisions and is used to correct and perfect future actions and decisions.

What’s the difference between AI and machine learning?

Machine learning and artificial intelligence are often lumped together in the same definition, but they aren’t necessarily the same. AI refers to complete systems (incorporating both hardware and software) that interact with the world and may use various models, including those from machine learning. Machine learning, on the other hand, focuses specifically on detecting patterns in data and constructing models to represent aspects of reality. While machine learning models can be components within AI systems, AI encompasses a broader range of technologies and methodologies beyond just machine learning.

In our forums, one of our learners, J, provides a helpful explanation:

“Artificial intelligence can be described as when machines carry out tasks in an intelligent or smart way, based on set rules to solve certain problems. Artificial intelligence, or AI, makes decisions, learns, and solves problems similar to how humans would.

Machine learning, on the other hand, is a subset of artificial intelligence. It’s when we give machines data and have them learn from that data on their own, without being explicitly programmed. Machine learning models learn from the data and try to make improvements to its predictions over time.”

So machine learning is a subset of the AI field, but not all AI is machine learning. AI is a broader field. Check out our blog on the differences between AI, machine learning, and deep learning to learn more.

What does an AI Engineer do?

Companies need AI Engineers to put their AI systems into place, maintain them, and adapt them to changes in the business. According to the World Economic Forum, employment of Machine Learning/AI Engineers is expected to grow on average by 30% by 2027. Let’s break down an AI Engineer’s roles and responsibilities:

In short, AI Engineers develop new applications and systems to:

• Enhance the performance and efficiency of business processes
• Help the business make better decisions
• Lower costs
• Increase revenue and profits

Simply put, they use software engineering and data science to streamline a business with automation. Some of the responsibilities of an AI Engineer include:

Building AI Models

One of the first responsibilities of an AI Engineer is to coordinate with business leaders and software development teams to determine what business processes can be improved by using AI. Once a business use case has been determined, AI Engineers play a key role in designing the algorithms and building the AI models. Developing AI-driven solutions that mimic human behavior to accomplish repetitive tasks currently done by people.

Data Processing

Before building an AI model, an AI Engineer must first collect, clean, and organize the raw data so it’s suitable for training. This includes developing data pipelines to streamline the process of transforming this raw data into the structured data necessary for AI processes. AI Engineers also handle tasks such as data normalization, transformation, handling missing values, and ensuring data quality and consistency. This ensures processed data is accurate, unbiased, and representative, which is critical for building reliable and effective AI systems.

Feature Engineering

AI Engineers must also be familiar with feature engineering. This involves identifying, creating, and selecting the most relevant features from raw data to improve model performance. That requires techniques like encoding categorical variables, scaling numerical features, extracting new features, and reducing dimensionality.

Model Deployment and Optimization

Another responsibility that falls to AI Engineers is to deploy trained AI models into production environments. To do so, AI Engineers design scalable deployment pipelines, manage model versioning, and monitor performance in real-time. This enables them to then optimize models for speed, memory usage, and accuracy

AI System Integration

AI Engineers must also integrate AI models into existing software systems and ensure seamless interaction with other components. They design APIs, manage data flow between systems, and ensure compatibility with infrastructure, such as cloud platforms or edge devices. This work ensures that AI solutions operate effectively within larger applications, are scalable, maintainable, and meet performance and security standards.

Monitoring and Maintenance

Over time, AI Engineers are tasked with continuously tracking the performance of deployed AI models to ensure accuracy, reliability, and efficiency. They set up monitoring tools to detect issues such as data drift, model degradation, or anomalies in predictions. When problems arise, it’s their responsibility to troubleshoot, retrain, or update models as needed to maintain optimal performance.

What skills are required of an AI Engineer?

AI is a broad field, and an AI Engineer requires both the skills of a Software Engineer and those of a Data Scientist. It may even help to know mathematics and statistics.

Programming Languages

An AI Engineer definitely needs to know at least one programming language. They’ll usually end up learning multiple during their career. Many of the tools that AI Engineers use to make their job easier will require knowledge of Python, R, or Java.

Machine Learning Frameworks

To build and work with machine learning models, an AI Engineer will also need to know the fundamentals of machine learning frameworks. Consider TensorFlow, Theano, PyTorch, and Caffe. They’ll also need to know how to turn raw data into the features that machine learning models use. In our skill path Build Deep Learning Models with TensorFlow, you’ll learn how to train, test, and tune neural networks for regression and classification.

Additionally, an AI Engineer must have experience with a variety of machine learning models, such as:

• Neural networks
• Recurrent neural networks
• K-nearest neighbors algorithms
• General adversarial networks
• Supervised learning
• Unsupervised learning
• Random forests
• Reinforcement learning

Mathematics and Statistics

To actually create new models and understand how they work, an AI expert may have to know linear algebra, probability, and statistics instead of using pre-built models. These topics help you understand hidden Markov models, Naive Bayes, Gaussian mixture models, and linear discriminant analysis. These are just some of the techniques used in machine learning.

Big Data

Data is also a vital part of an AI Engineer’s job. A lot of that data is stored in relational database management systems. So, having a basic knowledge of SQL, the language of databases, comes in handy. Still, some of this data will be stored in unstructured or semi-structured data stores. That means knowing big data technologies like Apache Spark, Apache Hadoop, Cassandra, and MongoDB is a big plus.

Technical Skills

AI Engineers require more than technical skills, though. They must also:

• Be meticulous and detail-oriented because small inconsistencies in data can cause big discrepancies in machine learning models.
• Have excellent communication skills because many of the people they work with won’t understand much of what they do. They’ll have to explain the results of their tasks in a way that anyone can understand.
• Be good at big-picture thinking so they can understand business needs and build AI systems that benefit the company.

What is an average AI Engineer salary?

AI Engineers make good money. According to 2024 data from the tech salaries site Levels.fyi, an entry-level AI Engineer in the U.S. earns $239,000 per year on average.

As engineers move up in their careers, the pay difference gets even bigger. At the Senior Level, big companies like Cruise and Amazon pay AI Engineers a lot more—$450,000 and $427,500 respectively

As more organizations look to integrate AI into their services and offerings, the demand for AI Engineers who can implement the technology is increasing. In Skillsoft’s 2024 IT Skills and Salary Report, a whopping 47% of IT decision-makers report that AI is their top area of investment for 2025. By mastering the in-demand skills and getting experience working with generative AI, you can stand out in the competitive job market.

How to become an AI Engineer

Gone are the days when a computer science degree or even any college degree would be required to become an AI Engineer. Good AI Engineers are in too much demand to require a degree. In fact, employers have learned that many skilled AI experts don’t even need one. They do it because they love the work.

If AI is the career path for you, and you don’t have a degree or want to spend four years learning artificial intelligence, you don’t have to. There are plenty of educational opportunities to learn AI online whenever you have the time and wherever you are in the world. Plus, most of the tools you need for the learning process are open-source and freely available online.

If you’re new to AI and looking for the best place to start your journey, why not try Codecademy? We have lots of courses that are designed to get you comfortable with using generative AI in your work and daily life. Start with Intro to OpenAI API to dig into large language models and effective prompts. Or you can focus on AI skills that complement your software development tasks, like Learn How to Use AI for Coding and Learn How to Use AI for Data Analysis.

Since knowing at least one programming language is a prerequisite for becoming an AI Engineer, a great place to start is our Learn Python 3 course. Python is one of the top languages used by Data Scientists and AI Engineers. It’s also a requirement of our Build a Machine Learning Model skill path. If you’re committed to becoming an AI professional, check out our Machine Learning/AI Engineer career path that covers all of the skills you’ll need as an AI Engineer.

Keep learning today

Never stop learning. AI is a broad field, and learning Python and machine learning fundamentals is a great start, but each skill you add to your resume can increase your value to a company.

For even more courses to build your AI skills, check out our full course catalog and revisit the skills section of this article. Good luck with your AI career path!

This blog was originally published in January 2022 and has been updated to include the latest salary data and new AI courses and paths.