How to become an AI engineer

Since the public release of ChatGPT in 2022, innovations in artificial intelligence (AI) have exploded—and they don’t appear to be slowing down anytime soon. Industries as diverse as finance, healthcare, and education are increasingly incorporating AI into technology platforms and business operations. Along with rapid advancements in AI comes the need for professionals who can design, implement, leverage, and manage AI systems: AI engineers.

Responsible for developing, programming, and training the complex networks of algorithms that comprise AI, AI engineers are in high demand—and highly paid. According to Glassdoor, AI engineers in the US earn an average salary of $152,433. At some companies, AI engineers earn much more; at Google, for instance, AI engineers earn $241,801 per year, on average.

With surging employer demand and competitive salaries, AI engineering makes for an attractive career path for individuals interested in AI. This guide walks you through what it takes to break into a career in this field, including basic AI skills needed, different routes toward becoming an AI engineer, and answering frequently asked questions from aspiring AI engineers. Ready to get started?

Jump to a section:

• What is AI engineering? Definition, roles, and job outlook
• Skills required to become an AI engineer
• 6 routes for becoming an AI engineer in 2024 and beyond
• Frequently asked questions
• Accelerate your AI engineering career with CodeSignal Learn

What is AI engineering? Definition, roles, and job outlook

AI engineering is the cutting-edge discipline that lies at the intersection of computer science, mathematics, and sometimes even cognitive psychology. It centers on creating systems that can learn from data, make decisions, and improve over time. AI engineering involves the design, development, testing, and refinement of intelligent algorithms and models that enable machines to perform tasks that typically require human intelligence. By harnessing the power of machine learning, deep learning, and neural networks, AI engineers develop solutions that can process and analyze vast amounts of data, recognize patterns, and make informed decisions.

The demand for AI engineers has seen a surge in the past few years, reflecting the rapidly growing integration of AI technologies across industries. The U.S. Bureau of Labor Statistics projects a 23 percent increase in jobs for all computer and information research scientists, including AI professionals, over the next decade—much faster than the average for all occupations. 

A more recent analysis found that job postings that call for skills in generative AI increased by an incredible 1,848 percent between 2022 and 2023. The proliferation of AI applications in everyday life and the rapid advancement of AI technologies suggest that the demand for skilled AI engineers will only continue to grow.

What does an AI engineer do? A real-world example

AI engineers are at the forefront of developing AI solutions that drive innovation and efficiency at their organizations. Their work often involves creating machine learning models that can analyze large datasets to predict outcomes or behaviors and, in turn, inform business decisions. AI engineers ensure that the underlying logic and processes of AI applications are robust and effective. Their work also extends into data science: AI engineers utilize statistical analysis and computational theories to extract actionable insights from complex data structures. This forms the backbone of predictive analytics, where AI engineers forecast future trends and patterns, which allows organizations to strategize proactively.

A day in the life of an AI engineer involves both theoretical problem-solving and practical application of skills. A typical AI engineer’s work day might start with reviewing the latest research on neural networks or machine learning techniques relevant to their area of specialization. They’ll likely also have meetings with cross-functional teams, where AI solutions are discussed in the context of current projects and business goals. The bulk of their day may be dedicated to hands-on tasks such as coding new algorithms, refining existing machine learning models, or analyzing datasets for hidden patterns. 

AI engineer responsibilities

An AI engineer’s responsibilities include a wide array of tasks critical to the development and deployment of AI systems, starting at its core with data preprocessing. Data preprocessing involves cleaning, structuring, and enriching raw data to ensure its suitability for model training. Following this, model training and evaluation are the next core tasks; this is where AI engineers apply various algorithms to the processed data and iteratively refine the models to enhance their accuracy and reliability. 

Once a model has been trained and evaluated, the next step is AI deployment, where the model is integrated into existing systems and applications—this makes AI functionalities accessible to end-users. The role of an AI engineer does not end with deployment, though. They must engage in continuous learning and model improvement, as AI systems evolve in response to new data and changing environments to remain effective. To produce effective models, AI engineers work closely with other teams—including data scientists, developers, and business analysts—to ensure that AI solutions align with broader organizational goals and user needs. 

What qualifications do you typically need to become an AI engineer?

Many entering the field of AI engineering hold a Bachelor’s degree, or higher, in Computer Science or in a related field like mathematics, statistics, or engineering. Given the rapid evolution (and relatively recent emergence) of AI as a discipline, formal education specifically in AI is less common. Many professionals in this field have pivoted from related areas, leveraging self-teaching resources, online courses, and bootcamps to gain the specialized knowledge required for AI work. Certifications in AI and machine learning from reputable platforms can also help aspiring AI engineers build competency in this area. 

In addition to education, those seeking a career in AI engineering should gain hands-on experience with AI projects. Employers often look for practical evidence of an individual’s ability to apply theoretical knowledge to real-world problems. This experience can come from personal projects, internships, or professional roles that involve tasks like data preprocessing, algorithm development, and model deployment. Aspiring AI engineers should also be knowledgeable about software development practices in general, as AI engineering involves both building models and integrating them into larger systems. Familiarity with cloud computing services is also important, as these platforms often host AI applications and offer scalable resources for training and deploying models.

Understanding the different types of AI engineer roles

The term “AI engineer” encompasses several different, related roles. Here are a few of the most common AI engineering titles—and how they differ:

What is the difference between an AI engineer and an ML engineer?

The difference between an AI and an ML engineer is primarily in the scope and focus of their work. An AI engineer deals with a broader range of artificial intelligence technologies, encompassing not only machine learning but also aspects like robotics, natural language processing, and cognitive computing. Their goal is to create systems that can mimic human intelligence in various ways. On the other hand, a machine learning engineer specializes more narrowly in algorithms that learn from and make predictions on data. They delve deeply into data models, focusing on developing, training, and fine-tuning algorithms; this allows machines to improve their performance over time without being explicitly programmed for each task.

Skills required to become an AI engineer

AI engineers need strong skills in math, programming (including specific languages, frameworks, and tools), and communication and collaboration. Here are more details about those core skills areas: 

Mathematical foundation

Strong skills in math are crucial to building a career as an AI engineer. Here are a few key areas of mathematics that aspiring AI engineers should master:

• Statistical analysis is at the heart of understanding data trends and making data-driven decisions and is essential for optimizing the performance of AI models. 
• Probability theory plays a key role in modeling the uncertainty of various real-world scenarios, which AI systems must navigate and interpret. 
• Linear algebra is fundamental for dealing with the large matrices and high-dimensional data structures common in machine learning and deep learning tasks. 

What if I’m bad at math?

If you feel you’re not strong in math, don’t let that deter you from pursuing a career in AI. Many resources are available that can help you strengthen your mathematical skills, including online courses, tutorials, and workshops specifically designed for learners at various levels. Starting with foundational topics in statistics can build your confidence and understanding gradually.

Also, keep in mind that much of AI development also relies on software tools and libraries that abstract complex mathematical operations, which allows you to focus initially on learning AI concepts and applications while you improve your math skills at the same time. 

Programming

Programming skills are pivotal for any AI engineer, and Python stands out as the quintessential language for AI due to its extensive libraries and frameworks that simplify the implementation of machine learning algorithms. Familiarity with frameworks like TensorFlow and PyTorch is essential, as these tools provide robust environments for building, training, and deploying machine learning models efficiently. AI engineers should also have a solid understanding of algorithms and data structures in Python to optimize solutions and manage complex data sets effectively. Knowledge of GitHub for version control is important, too, as it facilitates collaboration, code sharing, and version tracking within and across teams.

In addition to these foundational programming skills, an AI engineer must be adept at handling real-time data processing: vital for applications that require immediate analytical results and decision-making, like in autonomous vehicles or financial trading algorithms. Proficiency in using Python for these tasks allows engineers to implement solutions that can process and analyze streaming data efficiently. As AI continues to evolve, being skilled in both the theoretical aspects of machine learning and the practical applications of programming ensures that AI engineers are well-equipped to tackle emerging challenges and drive innovation in the field.

What programming language should I learn?

If you’re aiming to become an AI engineer, the first programming language you should learn is Python. It is the most widely used language in the field due to its simplicity and readability. Plus, it offers powerful libraries and frameworks such as TensorFlow, PyTorch, and Scikit-Learn that are specifically tailored for developing machine learning and deep learning models. Python’s extensive community support and wealth of open-source resources also make it an ideal starting point for beginners. As you progress, exploring other languages like R for statistical analysis, Java for system integration, or C++ for performance-critical applications can further enhance your skill set.

Learning tip: CodeSignal’s learning path in AI Theory and Coding teaches you to build machine learning algorithms—the foundation of AI—from the ground up. Follow this series of 6 courses to gain in-depth knowledge of AI theory, implement your own gradient descent, and learn ensemble methods, unsupervised learning, and more. 

Tools and frameworks

AI engineers must be proficient in a variety of tools and frameworks that are foundational to developing effective AI solutions. TensorFlow and PyTorch are two of the most prominent frameworks for deep learning that allow for easy model building, training, and deployment. For more traditional machine learning tasks, Scikit-learn offers a range of simple and efficient tools for data mining and data analysis. Data manipulation is another critical aspect of AI, and tools like Pandas and NumPy are excellent for handling and transforming data. Jupyter Notebook is another useful tool that allows for prototyping, experimenting with models, and interactive coding, which is particularly useful for visualization and analysis during development. 

Additionally, knowledge of GitHub is essential for code sharing and collaboration, as it enables you to manage projects efficiently while working with globally distributed teams. Lastly, being adept with Spark can significantly enhance your ability to handle big data processing, particularly for applications that require analyzing large datasets in real time. 

AI-specific skills: machine learning and natural language processing

While AI engineers need many of the same skills as other kinds of software engineers, they also need specialized knowledge and skills related to building and optimizing AI models. Two core areas to focus on when starting your journey toward becoming an AI engineer are machine learning (ML) and natural language processing (NLP). Machine learning is a subset of AI that uses algorithms that learn from data to make predictions. Natural language processing—another subset of AI—refers to machine learning technology that gives computers the ability to interpret and manipulate human language.

Core ML skills for AI engineers include: 

Core NLP skills for AI engineers include: 

Learning tip: Want to dive deeper in the AI-specific skills needed for AI engineering roles? Download CodeSignal’s latest whitepaper on the core skills needed for AI, ML, and prompt engineering roles, and how to fill these skill gaps on your technical teams. 

Soft skills are important if you want to start a career in AI

Soft skills are often just as important as technical abilities when it comes to getting hired as an AI engineer. Here are a few to focus on:

6 routes for becoming an AI engineer in 2024 and beyond

Getting into AI development isn’t easy, but it’s possible—and there are many ways to do it. Below, we describe 6 routes aspiring AI engineers can take to break into the field.

Participating in online courses and bootcamps

Participating in online courses and specialized AI bootcamps is an effective way to break into an AI engineering career. Online courses in AI topics allow learners to explore a range of topics at their own pace, from anywhere in the world. They are often a good fit for aspiring AI engineers who have a background in another technical field, like software development, by helping them fill skill gaps specific to AI engineering. 

Specialized AI bootcamps, on the other hand, offer an intensive, focused curriculum that immerses participants in practice-based learning. Bootcamps often involve hands-on projects to build students’ theoretical knowledge and practical skills applicable in professional settings. Both of these non-traditional educational paths can equip you with the necessary technical skills and practical experience to make a confident entry into the field of AI engineering.

Learning tip: CodeSignal’s Journey into Machine Learning with Sklearn and Tensorflow learning path builds your skills in core ML skills using popular Python libraries. Take this series of 5 courses to learn fundamental machine learning models with Sklearn, master regularization techniques, train neural networks using TensorFlow, and more.

Pursuing a formal education in AI

Pursuing a formal education in AI, such as bachelor’s and master’s degrees, is a common—though time- and cost-intensive—starting point for a career as an AI engineer. As specific degree programs in AI are still relatively rare, most aspiring AI professionals opt for degrees in closely related fields like computer science, where they can complete coursework that covers essential topics such as statistics, programming, and foundational AI concepts. Some computer science and engineering programs now offer specialized courses or tracks in AI and machine learning, as well. Engaging in thesis or research projects focused on AI can also enhance your understanding and exposure to the field. For those without access to formal degree programs in AI, self-teaching through online courses, bootcamps, and hands-on projects is a viable alternative.

What should I study for a career in AI?

For a career in AI, focus on studying a blend of subjects that build a strong foundational knowledge and practical skills. Core areas should include computer science for understanding programming and software development, statistics and mathematics for analyzing data, and specific AI coursework that covers machine learning, neural networks, and deep learning techniques. Supplement this formal education with hands-on projects that involve real data to help you apply theoretical concepts practically. 

Building a strong portfolio of AI projects

Building a strong portfolio of AI projects is a great way to showcase your skills and stand out in the competitive field of AI engineering. Start by developing real-world AI projects, which demonstrate your ability to apply AI techniques to solve practical problems. Utilize datasets from platforms like Kaggle to work on projects that are relevant and challenging, and which also provide the opportunity to engage in AI competitions and challenges. Participating in hackathons is another excellent way to gain experience, learn quickly, and meet other AI enthusiasts. 

Collaboration on open-source projects can further enhance your portfolio by showing your ability to work with teams and contribute to community-driven developments. You might also consider creating a personal blog or website to display your projects and explain how you built them. This website serves as a dynamic portfolio, can help you connect with others in the field, and may even contribute to AI research. 

What should I do as a project?

When selecting a personal AI project to enhance your portfolio, aim for something that aligns with your interests and the skills you want to develop. A practical approach is to identify a problem that AI can solve or improve, in any sector that’s of interest to you. Using publicly available datasets from platforms like Kaggle, you can tackle real-world issues, such as predicting disease outbreaks, financial forecasting, or even creating AI-driven environmental monitoring systems. Consider integrating a variety of AI technologies—like machine learning, natural language processing, or computer vision—to demonstrate a breadth of skills.

Getting certified

Getting certified through professional certification programs is another popular route to start a career in AI engineering. These programs, often offered through specialized AI bootcamps and continuous education platforms, provide credentials that can enhance your resume and professional credibility. Such certifications are designed to demonstrate your expertise in specific areas of AI (like machine learning, deep learning, and data analysis) to potential employers. They focus on upskilling and ensuring that professionals are up-to-date with the latest technologies and methodologies in the rapidly evolving AI landscape.

Attending industry events and networking with professional AI engineers

Another way you can pivot to a career in AI engineering is by attending industry events and networking with professional AI engineers. Participation in AI conferences, workshops, webinars, and virtual events provides valuable opportunities to learn about the latest advancements and trends directly from industry leaders. Joining AI meetups and local groups can also help you learn from and network with peers and experts in the field. Establishing a network of contacts within the AI community can open doors to  mentorship, collaborations, and sometimes even job opportunities. 

Getting experience through internships

Finally, securing an internship in AI engineering is an effective way to break into a career in this field. These positions offer hands-on experience and allow you to apply academic knowledge to real-world problems under the guidance of experienced professionals. Internships often involve working on specific projects where you can develop and refine skills related to machine learning, data analysis, and algorithm development. This practical exposure both enhances your skills and boosts your resume, making you a more competitive candidate for future job openings. Internships also provide a valuable opportunity to build professional networks and gain insights into the industry, and can even help you find mentorship and discover job opportunities post-internship.

Frequently asked questions

Here are some of the most common questions we hear from aspiring Ai engineers about how to get started.

How can I start a career in AI?

To start a career in AI, focus first on acquiring foundational knowledge through education, whether that’s online courses, a specialized AI bootcamp, an undergraduate or graduate degree program, or a combination of these. Gain practical experience by engaging in internships, developing personal AI projects, or contributing to open-source initiatives. It also helps to expand your professional network and stay current on AI innovations by attending AI conferences, workshops, and local meetups.

How hard is it to get into AI development?

Getting into AI development can be challenging due to the complex blend of technical skills and theoretical knowledge required, including proficiency in programming, statistics, and machine learning concepts. However, the abundance of resources such as online courses, bootcamps, and community projects makes it increasingly accessible for those committed to learning and developing their skills. Success in entering the field often depends on dedication to ongoing education, practical experience through projects or internships, and active participation in the AI community to build your professional network.

How long does it take to become an AI engineer?

The time it takes to become an AI engineer can vary widely depending on your starting point and how intensively you pursue your studies and experience. Typically, acquiring a bachelor’s degree in computer science or a related field takes about four years, followed by additional specialized training in AI through courses, bootcamps, or a master’s program, which can take anywhere from a few months to two years. Gaining hands-on experience through internships, personal projects, or in your current role can take additional time.

How can I identify what I need to learn and cover any knowledge gaps?

To identify what you need to learn to pursue  a career in AI engineering, start by assessing your current skills against the requirements of job listings or roles that interest you. Use self-assessment tools in online courses that specialize in AI  to pinpoint areas for improvement. It’s also worthwhile to seek feedback and advice from professionals in the field through networking, mentorship, or participating in forums and community groups.

Accelerate your AI engineering career with CodeSignal Learn

This guide has walked you through the responsibilities of different types of AI engineers, skills needed for this career, and the routes you might take to break into a career in AI engineering. Landing a role as an AI engineer isn’t easy, but, fortunately, there are many resources available to help you prepare.

A great place to start is with CodeSignal Learn, an online learning platform that provides a practice-based and outcome-driven learning experience featuring one-on-one support from our AI tutor and guide, Cosmo. CodeSignal Learn offers learning paths in AI and machine learning that take you from building foundational skills in data preprocessing, to training neural networks, to even building neural networks from scratch. 

Start learning for free today: