How to Become an AI & ML Engineer: Roles, Responsibilities & Salaries

AI & ML engineers are in high demand due to their skills and experience. Learn what these engineers do and how to launch your career in AI or ML.

AI and ML Engineer testing out an AI chatbot application on a mobile phone device

Artificial intelligence (AI) and machine learning (ML) engineers focus on developing emerging technologies for practical applications. Machine learning engineers create the tools, processes, and systems to extend machine learning beyond the controlled environment of the laboratory. AI engineers guide the development and deployment of AI and machine learning in dynamic, complex environments where they are needed most for everyday applications.

Artificial intelligence and machine learning engineers are in high demand to work in fields like image processing, diagnostics, and business process management. If you are an experienced software engineer or developer, understanding the AI engineer's learning path may lead you to an exciting career in AI or as a machine learning engineer. Specializing in one of these fields begins with learning the roles and responsibilities of these two types of engineers. In this guide, we’ll share insight into the roles, responsibilities, and career paths for machine learning and AI engineers.

What Is an AI Engineer?

An AI engineer uses machine learning algorithms and deeply layered networks to build new AI applications and systems. These AI models make it possible for machines to teach themselves and simulate human thinking to manage complex processes like decision making, improved performance, cutting costs, and increasing profits. AI engineers understand data science, programming, and software engineering. They can process large sets of data and run and maintain AI systems.

What Is an ML Engineer?

A machine learning engineer builds systems that are capable of learning from big data and using this knowledge to make predictions. Machine learning engineers use AI and large data sets to develop learning algorithms. With each operation, the system gathers new data to help it carry out future operations. An ML engineer can optimize the learning process by collecting, organizing, and analyzing data and performing tests on the AI system.

What Is the Difference Between an Artificial Intelligence and Machine Learning Engineer?

AI engineers build systems that exhibit human intelligence but work faster and more accurately than their human counterparts. ML engineers focus on one particular component of an AI system to optimize the output. Machine learning improves functionality with each repetition, learns from the data, and then can predict the outcome of that function. AI engineers produce standalone systems that incorporate machine learning, a subset of artificial intelligence.

What Do AI & ML Engineers Do: Roles & Responsibilities

AI and ML engineers are important in multiple industries since artificial intelligence applications continue to be developed for new sectors. General responsibilities for AI and ML engineers include:

  • Monitor and control development projects
  • Analyze systems used in project development
  • Apply AI reasoning and uncertainty to system development
  • Establish and achieve performance outcomes
  • Execute problem-solving initiatives using machine-learning concepts
  • Apply AI best practices to applications in robotic control, data processing, speech recognition, and data mining

Skills Required to Become an AI Engineer or ML Engineer

In order to have a successful career, AI and ML engineers should possess the following skills:

  • Algorithms: Algorithm theory is necessary to understand gradient descent, convex optimization, Lagrange, quadratic programming, partial differential equations, and summations. These are subjects you will use frequently.
  • Analysis & Statistics: You need to know concepts like matrices, vectors, and matrix multiplication. An understanding of the applications for derivatives and integrals is also essential.
  • Cloud Knowledge: Building and deploying AI and ML technology requires the support of cloud-based functionality.
  • Neural Network Knowledge: The architecture of neural networks is a pivotal skill for success in AI and ML engineering.
  • Problem-Solving, Communication, Leadership: These skills are important for interacting with upper management and leading a team.
  • Programming: Python is the primary programming language used in machine learning. Proficiency in other languages, such as R, Java, and C++ is important, too.

What Is the Salary of an AI or ML Engineer?

The skillset of AI and ML engineers puts them in high demand. As the use of AI continues to evolve, that demand will continue to increase. The national average U.S. base salary for a Machine Learning Expert is approximately $108,109+, which may vary by region/industry and often doesn't include bonuses or other incentives. For AI Specialists, the average base U.S. salary is approximately $125,007+, not including bonuses or incentives.

According to the Bureau of Labor Statistics, demand for workers with advanced computer science skills is projected to grow +21% over the next 10 years (2021-2031). Organizations around the world need professionals to accelerate their digital and data transformation, evolving infrastructure and operations to support the strategic use of large volumes of data ("big data") and enable practical applications of machine learning and AI to deliver smart, personalized user experiences.

Is AI or ML Engineering A Good Career?

Yes, machine learning engineering is a good career path across industries, with high salaries and a strong job market outlook. Machine learning is currently the most practical form of AI engineering, although strides are being made in deep learning and other subsets of AI.

But what defines a "good career path?" For each person, it's different: pay/salary, quality of life, interesting and meaningful work. Do some research on the types of machine learning and AI initiatives each company or organization is involved with. For example, you may be passionate about improving the predictive capabilities of an investment firm so it can use data to give better financial and retirement planning advice to clients. You may be excited about using machine learning algorithms to deliver smart, personalized user experiences.

Also consider each organization's maturity in the area of AI and machine learning. If a company is just starting to build a machine learning engineering team or function, an entrepreneurial individual who enjoys working hard to build something from scratch may thrive. If you are seeking something more established or predictable, a company whose machine learning efforts are more evolved may be a better fit.

How Do You Become an AI Engineer or ML Engineer?

Requirements for AI and ML engineering are the same in the beginning. Both require an aptitude for working with technology and a background in computer science or software engineering. At the master’s level, the AI engineer learning path diverges from that of the ML engineer to focus on different aspects of computer engineering because ML engineer job requirements are more focused on data and testing.

Both AI engineers and ML engineers need strong programming skills and a solid understanding of large-scale, complex computer systems. The MCS@Rice program educates students in these areas, including machine learning and how to manage large data sets.

Rice Prepares Students for a Career in AI or ML Engineering

Want to learn more careers in AI and ML? Graduates of Rice’s online Master of Computer Science degree program have secured roles as AI or ML Engineers across the tech, financial services and healthcare sectors. Explore the MCS@Rice curriculum offerings and our best-in-class student experience.

Demand is surging for computer science skills across industries and applications.

Get More Information

Sign up to receive more information on how the MCS@Rice program can help you broaden your career options. Connect with an Enrollment Coach today.