MLOps Engineer: Roles, Skills, and Career Path

Machine learning operations (MLOps) engineering is an emerging career path that sits between DevOps and machine learning. Its aim is to effectively develop, test, and deploy machine learning (ML) models. Whereas DevOps involves the development and deployment of software, MLOps follows the same process but primarily deals with machine learning models.

MLOps is a growing field due to the increase in businesses using machine learning to make decisions. As a relatively new job function, MLOps engineer roles may vary from company to company as businesses decide how to best incorporate machine learning operations. However, with the increased use of artificial intelligence in the coming years, the need for MLOps engineer professionals is bound to grow at a rapid rate.

Discover more about MLOps engineer roles and responsibilities and how to pursue this up-and-coming career.

What is an MLOps engineer?

As an MLOps engineer, you work in an operations role, using your expertise in machine learning in collaboration with data scientists, developers, IT operations staff, and stakeholders. You effectively bridge the gap between these roles, bringing ML models through their life cycle of development, testing, deployment, and scalability. 

MLOps vs. DevOps

MLOps and DevOps are similar in that they both focus on operational procedures in an IT environment. However, while DevOps involves the development and deployment of software, MLOps focuses on developing, producing, training, and monitoring machine learning models.

Roles and responsibilities of an MLOps engineer

As an MLOps engineer is a relatively new position, your duties and responsibilities may vary depending on where you work, who you work for, and your company's understanding of the MLOps process. In general, you can break down an MLOps engineering role into three overarching parts:

Development:

• Overseeing the ML model pipeline

• Approving changes and reviewing features

• Monitoring the success of testing

• Ensuring model artifacts are properly handled

Deployment:

• Training and testing ML models

• Using continuous integration/continuous deployment (CI/CD) techniques

• Using tools like Docker and Kubernetes to deploy ML models to production

Management and monitoring:

• Monitoring data and creating reports, and necessary documents

• Implementing automated model retraining functions

• Using monitoring tools to track error rates, response times, and resources to report anomalies

MLOps engineer vs. ML engineer 

As an MLOps engineer, you’re responsible for machine learning models' workflows and life cycles to get them to production. This is different from an ML engineer role, where you’re responsible for the actual designing and developing of ML models. You will likely find a crossover between these roles, especially in smaller companies where your responsibilities may take on a wider scope.

MLOps engineer skills

As an MLOps engineer, you need a combination of machine learning, development, and operational skills. Both technical and workplace skills are essential, as this role involves highly technical functions but also relies on collaboration and teamwork. 

Technical skills:

• Machine learning algorithms

• DevOps

• Data science

• Automating workflows

• CI/CD

• Software development

• Agile methodologies 

• Programming languages: Python, C++, Java

• Software testing

• Statistical modeling

• Database construction and administration: SQL

Workplace skills:

• Collaboration

• Communication

• Organization

Job outlook for MLOps engineers

Businesses are experiencing a skills gap regarding MLOps and are having difficulty recruiting staff with the right machine learning skills. In fact, one-in-three IT leaders struggle to find qualified employees for ML roles [1]. 

The World Economic Forum predicts a 40 percent growth in demand for artificial intelligence and machine learning specialists through 2027 [2]. If you can demonstrate relevant qualifications and experience in both DevOps and machine learning, you will be ahead of the curve. 

In addition, the global MLOps market is projected to be worth more than $13 billion by 2030 (up from around $1 billion in 2023), which points to increased MLOps job opportunities in the future [3]. Industries that rely heavily on machine learning are likely to see the most MLOps job growth include:

• Banking

• Health care

• Manufacturing

• Marketing and sales

• Retail

MLOps engineer salary

According to ZipRecruiter, the average annual US salary for an MLOps engineer is $87,220, with the highest earners making $136,500 [4]. While Glassdoor doesn’t have salary information for an MLOps engineer specifically, average annual base salaries on Glassdoor for a machine learning engineer and a DevOps engineer are $121,339 and $107,910, respectively [5,6].

How to become an MLOps engineer

As MLOps is such a new field, you don’t necessarily need to follow a standard path to enter the profession, but it is a senior-level role that usually requires a software development background. To work in a role at this level, you’ll generally need a bachelor’s degree in a relevant major, such as computer science, data science, software engineering, math, or statistics, along with some related experience in the field.

The more skills, education, and experience you can demonstrate, the better your chances of securing a position, so consider building your credentials with online courses and certifications. 

Similar careers to an MLOps engineer

The skills and experience you need to work as an MLOps engineer can also serve you well in other similar careers and vice versa. You may move into this line of work from a DevOps role or from a background in machine learning, or one of the below careers may lead to an MLOps job.

According to the US Bureau of Labor Statistics, employment in the software development field is projected to increase by 17 percent from 2023 to 2033, with an average of 140,100 new openings each year [5]. Job growth for data scientists is expected to increase by 36 percent over the decade [6].

1. Machine learning engineer

Average annual US salary (Glassdoor): $121,250 [7]

Requirements: As a machine learning engineer, you may need a bachelor’s degree and a master’s degree in data science, software engineering, electrical engineering, computer science, or similar. 

As a machine learning engineer, you would design and build machine learning algorithms and models for automation. These models are a type of artificial intelligence with learning capabilities that develop over time, making operations more accurate as they retain and learn.

2. DevOps engineer

Average annual US salary (Glassdoor): $107,829 [8]

Requirements: You may need a bachelor’s degree in computer science or a similar field as a DevOps engineer. You may also consider pursuing a master’s degree for career progression.

As a DevOps engineer, you would collaborate with both the development and operations teams on software development and deployment. Working within the software development life cycle, you would automate and optimize processes to ensure smooth operations and enhance collaboration between departments.

3. Site reliability engineer (SRE)

Average annual US salary (Glassdoor): $123,354 [9]

Requirements: As a site reliability engineer, you may need a bachelor’s degree in computer science, software design, computer engineering, or similar. Some employers expect a master’s degree.

As an SRE, you would enhance a system’s performance and ensure its safety by designing technical solutions. To do this, you would use software tools to automate tasks like application monitoring, which makes systems more reliable and scalable. 

4. Data scientist

Average annual US salary (Glassdoor): $115,545 [10]

Requirements: As a data scientist, you may need a bachelor’s degree in computer science, information technology, or similar.

As a data scientist, you may work on an MLOps team. Your role could involve analyzing and using data, including building machine learning models. You would summarize data by building reports, making diagrams and charts, and presenting them to decision-makers.

Next steps on Coursera to become an MLOps engineer

MLOps is a new discipline working within the DevOps and machine learning job functions. As machine learning is becoming more common, companies need processes to make sure they’re developing and deploying ML models effectively. 

If you have experience in DevOps and want to learn more about machine learning in preparation for an MLOps engineer role, consider the IBM Machine Learning Professional Certificate, which is available on Coursera.