How to Become an AI Engineer

by Danielle Antosz | Published on March 11, 2025

From virtual assistants to self-driving cars and fraud detection, AI is transforming nearly every aspect of our world. At the center of this revolution are AI engineers, the experts who design, build, and optimize the systems that make artificial intelligence possible.

AI engineers use machine learning, deep learning, and neural networks to develop systems that analyze data, recognize patterns, and automate decision-making. Their role is increasingly in demand as businesses in nearly every industry rely on AI to drive innovation and efficiency.

If you're interested in cutting-edge technology, solving complex problems, and working with AI-powered systems, becoming an AI engineer could be a highly rewarding career. In this guide, we'll break down the skills, education, and steps needed to get started in AI engineering.

What is an AI Engineer?

An AI engineer is a professional who develops, optimizes, and deploys AI systems that analyze data, recognize patterns, and make decisions without human intervention. If AI is the Wizard of Oz, AI Engineers are the man behind the curtain who ensures everything works correctly. 

Unlike data scientists, who primarily focus on analyzing and interpreting data, AI engineers take those insights and build functional AI models that can automate tasks, enhance user experiences, and solve complex problems. 

AI engineers work on various projects, from developing chatbots to creating predictive analytics models. Core responsibilities include:

• Designing and developing AI algorithms to improve automation, data analysis, and decision-making.

• Building and optimizing machine learning and deep learning models for applications such as image recognition, speech processing, and recommendation systems.

• Training AI models with large datasets, fine-tuning them for accuracy, and ensuring they perform efficiently.

• Integrating AI solutions into real-world applications, working with software developers and data scientists to deploy AI systems.

• Improving AI models over time by monitoring performance, retraining models, and updating algorithms based on new data.

AI engineers play a critical role in shaping the future of artificial intelligence, a highly competitive and fast-growing field. 

Technical Skills Needed to Become an AI Engineer 

AI engineers need a strong foundation in programming, machine learning, and data handling. Since AI systems rely on vast amounts of data and complex algorithms, engineers must also be proficient in coding, model training, and optimization techniques. 

Here are the key technical skills required for success in this role: 

• Programming languages: Proficiency in Python, Java, or C++ is essential.

• Machine learning frameworks: Experience with TensorFlow, PyTorch, and Scikit-learn is necessary for building and training AI models.

• Deep learning techniques: Knowledge of neural networks, convolutional neural networks (CNNs), and recurrent neural networks (RNNs) is critical for handling advanced AI applications.

• Algorithms and data structures: A strong grasp of sorting algorithms, graph theory, and optimization techniques helps in creating efficient AI models.

• Data manipulation and analysis: Familiarity with NumPy, Pandas, and SQL is needed to clean, process, and analyze large datasets used for AI training.

• Cloud computing and AI deployment: Experience with AWS, Google Cloud, or Microsoft Azure is beneficial for deploying AI models at scale and integrating them into cloud-based applications.

• Big data and distributed computing: Understanding Hadoop, Apache Spark, and parallel computing allows AI engineers to process massive datasets efficiently.

It's worth noting that AI technologies evolve quickly, so AI engineers must be willing to adapt and learn new skills and technologies fast. 

AI Engineer Industry Demand and Job Outlook 

The demand for AI engineers is soaring as industries increasingly adopt AI for automation, data analysis, and decision-making. While the BLS does not currently track the job outlook for AI engineers (likely because it is an emerging field), we can look at similar and related fields to get an idea of where the industry is going. 

According to the U.S. Bureau of Labor Statistics (BLS), employment in computer and research scientists is expected to grow 26% from 2023 to 2033, outpacing most fields. 

Data scientists, who often work closely with AI technologies, are expected to see a 36% growth in employment from 2023 to 2033. This surge reflects the escalating importance of analyzing and interpreting complex data to inform business strategies and develop AI models.

Keep in mind that AI engineers are not only employed in IT. Industries like legal, financial operations, and insurance are increasingly using AI. 

Education and Training Resources

Becoming an AI engineer typically requires a strong educational background in computer science, mathematics, or a related field. While some enter the field through self-study and boot camps, most employers prefer candidates with formal education, certifications, and hands-on experience. 

Academic Programs 

Most AI engineers start with a bachelor’s degree in computer science, data science, mathematics, or engineering, which provides a solid foundation in programming, algorithms, and data structures. Many also pursue advanced degrees, such as a master’s or Ph.D. in artificial intelligence, machine learning, or robotics, to specialize in deep learning, neural networks, and AI research. 

AI Engineer Certifications 

Earning AI-related certifications can boost your credibility and show you have expertise in AI tools and cloud platforms. Some of the most recognized certifications include:

• Google Cloud Professional Machine Learning Engineer: Focuses on building, deploying, and managing AI models on Google Cloud.

• AWS Certified Machine Learning – Specialty: Covers AI model training, data processing, and cloud-based AI solutions.

• Microsoft Certified: Azure AI Engineer Associate:  Demonstrates proficiency in AI services, machine learning, and natural language processing on Azure.

While certifications aren’t always required, they can help you stand out in a competitive job market and validate your technical skills. 

AI Engineer Career Path and Progression

AI engineers typically start in junior-level roles, gaining hands-on experience in data handling, model development, and AI implementation before moving into more advanced positions. 

Many begin their careers as junior AI engineers, data engineers, or research assistants, where they focus on cleaning datasets, assisting with algorithm development, and supporting AI model deployment under the guidance of senior engineers.

With experience, AI engineers can advance to roles such as senior AI engineer, AI architect, or machine learning engineer. For those seeking leadership roles, AI engineers can transition into positions such as AI architect or AI research lead, where they guide the strategic direction of AI initiatives within a company.

Depending on their interests and expertise, AI engineers can also explore alternative career paths in robotics, computational neuroscience, or AI ethics.   

How Much Does an AI Engineer Make?

AI engineers earn high salaries due to the high demand for specialized skills. Salaries vary based on experience, location, and industry, with tech hubs like San Francisco, Seattle, and New York offering some of the highest pay.

Entry-level AI engineers typically start with competitive salaries, while those with a few years of experience can quickly advance to six-figure earnings. Senior AI engineers and those in leadership roles, such as AI architects or chief AI officers, can command significantly higher salaries, especially in industries like finance, healthcare, and autonomous systems. 

Here’s a general overview of AI engineer salaries: 

Source: Glassdoor, as of December 2024

Is a Career in AI Engineering Right for You? 

Whether you're interested in AI-driven automation, deep learning, or cloud-based AI solutions, this career offers high salaries, strong job stability, and endless room for growth.

To break into AI engineering, focus on learning programming languages like Python, gaining hands-on experience with AI frameworks, and earning relevant certifications. As AI continues to evolve, staying adaptable and open to learning new techniques will set you apart in this competitive industry.

If you're ready to take the next step, consider enrolling in an AI-focused course, pursuing a certification, or applying for entry-level roles to gain real-world experience. The future of AI is unfolding—why not be a part of it?

Want to learn more about AI? Consider this Microsoft Certified: Azure AI Fundamentals (AI-900) online training.