Artificial intelligence (AI) is changing the game across various industries, and behind every smart algorithm and predictive model is an AI engineer making it all happen. If you've ever wondered what an AI engineer does, how much they make, or how to become one, you're in the right place.
What is an AI engineer?
An AI software engineer is a specialized software engineer who develops, deploys, and optimizes artificial intelligence models. These professionals work with machine learning, deep learning, and data science to build systems that can learn and make decisions without human intervention. An AI engineer can work in various sectors besides the tech industry, like healthcare, finance, entertainment, and e-commerce.
So, is an AI engineer the same as a software engineer? Both share overlapping skills, but AI engineers specialize in artificial intelligence models, whereas traditional software engineers focus on developing applications, websites, and software systems. Think of AI engineers as software engineers with a deep focus on AI and machine learning.
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What does an AI engineer do?
The specifics vary depending on the industry and specific role, but common responsibilities include:
- Developing AI models: Designing and implementing machine learning and deep learning models
- Data preprocessing: Cleaning and organizing massive datasets to train AI systems
- Training and fine-tuning algorithms: Adjusting models to improve accuracy and efficiency
- Integrating AI into applications: Embedding AI models into software, products, and services
- Collaborating with data scientists and engineers: Working with a team to build robust AI solutions
- Monitoring performance and debugging issues: Ensuring AI models function properly in real-world scenarios
If you’re wondering what an AI engineer does in a day, “it varies significantly depending on the individual, the team, and the company,” says Marcio Cantarino, an AI software engineer at LinkedIn. “For me, it’s a mix of strategic work, leadership, and hands-on coding and data analysis,” Cantarino says.
His typical day can be split into:
- Hands-on tasks: Analyzing data, training machine learning models, building data pipelines, and evaluating results—then repeating the process when bugs appear or models stop working
- Peer collaboration: Reviewing reports, proposals, plans, and code from coworkers (AI is a highly collaborative field)
- Team meetings: Sharing ideas, aligning on objectives, solving problems, and refining strategies
- Documentation: Recording progress and outlining next steps to keep stakeholders informed
AI engineer salary and job demand
AI engineers are among the high-paid professionals of the tech industries. Salaries depend on experience, location, and company. According to Glassdoor, the estimated total pay for an AI engineer is $206,344 per year.
The rise of generative AI, prompt engineering, and ethical AI development means that opportunities in this field will only grow. Job growth for AI engineers is projected at 26% from 2023 to 2033, significantly surpassing the average of 4% for all occupations.
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How to become an AI engineer
Interested in becoming an AI software engineer? Here's how:
1. Earn a degree
Now, you might be thinking, “What degree do I need to be an AI engineer?”
Most AI engineers have a degree in computer science, data science, or software engineering. A strong foundation in mathematics, statistics, and programming is essential for this career path. However, some professionals transition into AI engineering from other fields through specialized certifications, boot camps, and hands-on experience with AI projects.
“My background is in Physics, which is actually pretty common among AI/ML Engineers and Data Scientists,” Cantarino says. “In physics, and other natural sciences, our primary tools consist of mathematics, computational methods, and statistics. That’s what we use to describe, simulate, and analyze natural events.
“I was also lucky enough to specialize in Cosmology, which relied heavily on an equal measure of those parts,” he says. “So almost by accident I ended up developing the tool box necessary to work in AI.”
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2. Learn AI skills
So, what are the skills required for an AI engineer? You'll need:
- Programming languages: Python, Java, C++, and R
- Machine learning frameworks: TensorFlow, PyTorch, Scikit-learn
- Data science and statistics: Understanding of probability, statistics, and data structures
- Cloud computing: Experience with AWS, Google Cloud, or Azure
- Problem-solving and creativity: Thinking outside the box is crucial for innovation
“My education background gave me the tools to do the work—that being hands-on experience with programming and understanding of calculus, linear algebra, and statistics,” says Cantarino about the additional education he had to pursue to transition into AI. “So to enter AI, I had to focus specifically on the computer science fundamentals I was missing. That meant learning the basics of algorithms and data structures, understanding time and space complexity, and practicing extensively on websites like LeetCode.
“For anyone looking to transition, I recommend focusing on developing skills in whichever aspect you need to strengthen,” he says.
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3. Gain practical experience
You can’t just learn AI from textbooks—you need hands-on experience. Some ways to build your skills are:
- Completing AI projects on platforms like Kaggle
- Contributing to open-source AI projects
- Applying for AI engineer internships
4. Earn AI certifications
Certifications can make your resume stand out. Here are some of the best AI certifications to consider:
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5. Apply for AI engineer jobs
Once you have the skills and experience, it's time to land AI engineer jobs. Tailor your resume to highlight AI-related projects, showcase your portfolio, and start networking with professionals in the field.
What jobs can an AI engineer do?
AI engineers can take on a variety of roles:
- Machine learning engineer: Focuses on building and fine-tuning AI models
- AI research scientist: Works on developing new AI technologies and algorithms
- AI software developer: Integrates AI into applications and software solutions
- Data scientist: Uses AI to analyze and interpret large datasets
- AI consultant: Helps businesses implement AI solutions.
- AI prompt engineer: Designs effective prompts for generative AI models to improve responses
Insider tip: Think beyond the code
“Don’t underestimate the importance of product sense when studying AI/ML,” Cantarino says. “Think about AI applications from the perspective of the companies you’re interested in joining. What are the model performance requirements? What are expected tradeoffs? How do you handle incorrect model predictions? How do you measure the success of the model?”
Also, think beyond coding. “Understanding the underlying mathematics and being able to interpret data is equally, if not more, important,” he says. “Make sure you actually like working in this intersection of math, stats, and coding. I personally find it very intellectually stimulating, but I can imagine the work would feel daunting if I didn’t.”
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Is AI engineer a good career? Absolutely
AI engineers are at the forefront of technological innovation, developing systems that change how we live and work. Whether you're drawn to AI for the challenge, the salary, or the impact, now is the perfect time to start your journey. With the right skills and determination, you can break into this exciting and ever-expanding field.