How to Become an AI Engineer
| 5 Min Read
Becoming an AI engineer requires a combination of programming, machine learning, and applied mathematics skills. There is no single fixed path into the role, which makes it accessible to computer science graduates, self-taught developers, and career changers alike.
Learn what AI engineers do day-to-day and look at the jobs and salary you can expect. We’ll also cover how a master’s degree can help you stand out in this competitive field.
What Does an AI Engineer Do?
AI engineers build and maintain the AI systems that power many modern products and services. They take machine learning models out of the research environment and turn them into reliable tools that businesses can use. This includes recommendation engines that people use every day, all the way to fraud detection software that cybersecurity specialists rely on.
AI engineers have a diverse skillset where they might spend one minute writing production-quality code and the next making sure a model is deployed properly and continues to perform as expected.
What Responsibilities Does an AI Engineer Have?
While specifics can vary by company and industry, most AI engineers share a common set of responsibilities:
- Designing and training machine learning models to solve specific business problems.
- Building data pipelines that collect and prepare the information models learn from.
- Deploying models into production so they can operate within real products and services.
- Monitoring and maintaining AI systems to keep them accurate and reliable.
- Collaborating across development and business teams to align AI initiatives with organizational goals.
With a clearer understanding of the role, the next step is learning how to build the technical skills and experience needed to become an AI engineer.
How Do I Become an AI Engineer?
There’s no single path into AI engineering, but employers typically look for a mix of technical knowledge and practical skills. A postgraduate degree in AI can help you build both. Here’s what that path can look like.
- Build Your Technical Foundation
- You’ll start with learning Python, the dominant programming language in AI, alongside statistics and linear algebra. You’ll also learn everything from understanding how models learn and debugging them when they fail. These fundamentals are crucial for understanding how AI technologies work.
- Learn Core Machine Learning Frameworks
- Once you have a strong foundation, gain hands-on experience with tools such as scikit-learn, TensorFlow, and PyTorch. Familiarity with large language models and generative AI tools is helpful as well.
- Work on Applied Projects
- Build a portfolio of real projects, such as a predictive model, a computer vision application, or a deployed chatbot to build practical skills. Once you have a portfolio that demonstrates your skills, share your work publicly on platforms like GitHub.
- Pursue Certifications or an Advanced Degree
- LAU’s online AI programs help students build the technical foundations covered in step one and develop their understanding of the machine learning tools and methods discussed in step two. For those studying alongside their careers, opportunities to apply this knowledge can also arise naturally through course projects and their work.
- Gain Professional Experience
- Entry-level roles and internships in software development or data analysis all provide a route into full AI engineering work.
Starting a Career as an AI Engineer
According to the World Economic Forum, AI and big data rank among the world’s fastest-growing skills, reflecting the increasing demand for professionals with expertise in these areas.
With investments in AI expected to reach $1 trillion by 2026, opportunities for experts in the field, such as AI Engineers, is also surging. In fact, the US Bureau of Labor Statistics projects roughly 318,000 new openings each year for information technology professionals.
This level of demand also creates a lot of competition, because roles like AI engineer attract many qualified applicants. Developing strong technical skills and practical training can help candidates stand out to employers.
How an MS in Applied Artificial Intelligence Helps You Grow as an AI Engineer
A master’s degree in artificial intelligence can significantly shorten the time it takes for you to become an AI engineer by providing you with structured training and hands-on experience.
LAU’s Online MS in Applied Artificial Intelligence is designed to build the applied technical skills that employers in the field are actively hiring for.
Skills You’ll Build in LAU’s Online MS in Applied Artificial Intelligence
LAU’s Online MS in Applied Artificial Intelligence is a 30-credit program that combines seven core courses with electives in data visualization, big data analytics, healthcare analytics, agentic AI, and more. The program also includes a hands-on project or thesis.
The curriculum gives students practical experience in areas such as:
- Machine learning and its applications
- Computer vision
- Data science and data processing
- Mathematics for AI
- AI ethics and real-world implementation
This US-accredited program is conferred by LAU New York and allows working professionals to study while working with its flexible online format. Graduates develop skills applicable to roles such as information systems manager, artificial intelligence specialist, data science director, and computer and information research scientist.
Your Next Steps Toward Becoming an AI Engineer
Becoming an AI engineer is a realistic goal for anyone willing to build the right skills required to excel in the field. With opportunities growing across different industries, there are several pathways to enter the profession and build a career in AI.
If you’re ready to build the AI skills employers are hiring for, explore the Lebanese American University’s Online MS in Applied Artificial Intelligence.