Machine Learning Engineer vs. AI Engineer: Role Comparison
| 5 Min Read
Machine learning and artificial intelligence (AI) are quickly becoming integral components of today’s technology landscape. Both fields are transforming industries worldwide, and technologies such as predictive analytics, generative AI and autonomous systems are accelerating business growth and operational efficiency. As organizations adopt these technologies to improve decision-making and streamline complex processes, the demand for professionals who can design, build and manage intelligent systems continues to rise.
This guide explores the differences between machine learning engineers and AI engineers, and how those differences may affect your career decision-making. The Master of Science (M.S.) in Artificial Intelligence (AI) online program at Concordia University, St. Paul (CSP Global) helps professionals become innovative leaders with AI expertise.
What Is a Machine Learning Engineer?
A machine learning engineer is a specialized software engineer who bridges the gap betweendata science and production. Primary responsibilities include researching, building and deploying AI models to automate predictive tasks and turning experimental models into real-world software applications.
Key skills and technical competencies for qualified machine learning engineers include data preparation, model training, monitoring and infrastructure management. They must know how to develop (code), test (run experiments) and maintain (monitor and update) high-performance production code, process large datasets for model training and use tools for that training at scale.
Common projects for machine learning engineers include natural language processing (NLP), computer vision modeling and building recommendation engines. Work environments typically include research labs, tech companies and data-driven industries.
The typical career pathway usually includes taking on a foundational technical role, such as a junior software engineer, data analyst or software programmer, before moving into mid- to high-level roles. Minimum educational requirements include a bachelor’s degree, though some employers prefer candidates with a master’s degree.
What Is an AI Engineer?
An AI engineer is a software developer who specializes in designing, building and deploying AI and machine learning models to create functional, real-world applications that integrate tools such as large language models (LLMs) and neural networks. Core responsibilities and skill sets include developing AI algorithms, testing models, preprocessing data and optimizing performance.
While machine learning engineers focus on developing, training and deploying data-driven models, AI engineers take a much broader approach, focusing on the product itself. AI engineers work on end-to-end system implementation of existing AI technologies, while machine learning engineers focus on building and training models and technologies from scratch.
AI engineers should have a strong technical background with a bachelor’s degree in a STEM field as the base requirement. Most successful AI engineers hold advanced degrees and certifications that bridge theory and practice from specific companies, such as the IBM AI Engineering Professional Certificate or the Certified AI Engineer (CAIE) from the United States Artificial Intelligence Institute (USAII).
Key Differences Between Machine Learning Engineers and AI Engineers
While they may seem similar at first glance, there are some key distinctions between machine learning and AI engineers. Machine learning engineers’ core focus is on the model layer, including data pipelines, algorithm selection, training and performance optimization. AI engineers’ core focus is on the application layer of broader AI systems, such as integrating the systems into products, building chatbots, using AI tools and ensuring a smooth user experience.
The scope of work for machine learning engineers includes model development, while AI engineers implement those end-to-end AI solutions for various companies and industries. Machine learning engineers work with large, structured datasets to train models, while AI engineers work with a range of data types, including unstructured data for computer vision tasks or natural language processing (NLP).
Industry applications for machine learning engineers include implementing predictive modeling, computer vision and generative design to optimize operations for a wide range of industries and organizations. However, AI engineers utilize these technologies for various, more specific applications, such as AI-driven robots in manufacturing, advanced driver assistance systems for self-driving vehicles, algorithmic models in finance and banking and customer recommendations and services for e-commerce companies.
Career Outlook and Salary Comparison
The demand for both roles is rising rapidly, with a projected job outlook of approximately 40% more roles available between 2024 and 2025 than in prior years. Annual salary for both roles varies, but computer and information research scientists and similar roles with a master’s degree may earn approximately $140,910, according to the U.S. Bureau of Labor Statistics (BLS).
Industry trends are shifting from new and experimental to industry-wide implementation of AI and machine learning across almost every sector, from healthcare and automotive to manufacturing. According to the World Economic Forum, AI could add approximately 14% to global gross domestic product (GDP) by 2030, amounting to around $15.7 trillion.
The adoption of AI and machine learning varies by geographic location, with a major concentration of jobs in high-income technology hubs and large metropolitan areas, such as the Bay Area of California, New York and San Jose. Industry-specific variations include finance and IT, which require data-intensive tasks; logistics and transportation, which analyze location data; and manufacturing, which uses robotic AI.
Which Career Path Is Right for You?
Which of these future-forward career paths is the right one for you? Consider key foundations of AI and machine learning and how each relates to your unique interests, skills and future career goals. It’s also important to consider the required educational pathway for each role. Both require a minimum of a bachelor’s degree in a field such as computer science, mathematics or data science, along with specialized certifications.
An advanced education, like a master’s degree, supports a successful career transition because these degrees are highly sought after for more advanced roles that focus on specialized areas such as deep learning, natural language processing or robotics. CSP Global’s M.S. in Artificial Intelligence program boosts your career potential by teaching the latest AI skills that employers require.
Be Part of the Future With an M.S. in AI Degree
Pursuing a graduate degree in artificial intelligence and machine learning allows you to move beyond foundational knowledge and develop advanced expertise for high-demand roles in this rapidly evolving field. CSP Global’s online M.S. in AI program equips professionals with essential skills in neural networks, computer vision and machine learning, with an emphasis on ethical AI integrated throughout the curriculum.
With a flexible online format and six start dates per year, the program is designed to fit the schedules of working professionals. Explore CSP Global’s online M.S. in AI program to learn more about its industry-aligned curriculum and innovative approach.
Learn more about CSP Global’s M.S. in AI program.