MLOps Engineer: Roles, Skills, and Career Path

Key takeaways

• An MLOps engineer is an operations professional who uses machine learning expertise to bridge the gap between data scientists, developers, IT operations staff, and stakeholders throughout the ML model life cycle.

• MLOps engineer jobs require technical knowledge of machine learning algorithms, DevOps, data science, workflow automation, programming languages, and Agile software development.

• An MLOps engineer’s salary is $161,000 annually, representing the median total pay in the US [1].

An MLOps engineer performs tasks such as overseeing ML model pipelines, training and deploying models, monitoring data and model tests, implementing automated retraining, and tracking errors and resources. Discover more about MLOps engineer roles and responsibilities, and how to pursue this up-and-coming career.

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What is MLOps?

Machine learning operations (MLOps) is an emerging career path that sits between DevOps and machine learning. It aims to develop, test, and deploy machine learning (ML) models effectively. While 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 increased use of machine learning in business decisions. As a relatively new job function, MLOps engineer roles may vary from company to company as businesses decide how best to 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 rapidly.

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 development, testing, deployment, and scalability life cycle.

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.

Is MLOps harder than DevOps?

MLOps can be considered more complex than DevOps since it focuses on data in addition to code, while DevOps considers only code. This adds a layer of complexity to MLOps as it involves separate versioning for not only the code but also the data sets, feature tables, and ML models.

ML models can also suffer from model drift after deployment, where the performance of ML models degrades over time in terms of accuracy when the output data deviates from the training data. MLOps professionals need to constantly monitor the model outputs by tracking quality metrics, since an ML model can fail even if the underlying infrastructure is sound. This is a unique challenge for MLOps workflows, as DevOps pipelines generally end after deployment, only tracking metrics like latency and error rate.

Read more: DataOps vs. MLOps: What’s the Difference?

What does an MLOps engineer do?

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, 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 differs from an ML engineer role, where you’re responsible for designing and developing ML models. You will likely find a crossover between these roles, especially in smaller companies where your responsibilities may take on a wider scope.

Skills for MLOps engineer jobs

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, 34 percent of IT leaders rated their team’s AI/ML competencies as the lowest among 30 skills [2].

The World Economic Forum predicts a 142 percent growth in demand for artificial intelligence and machine learning specialists in the US through 2030, compared with 82 percent growth globally [3]. You will be ahead of the curve if you can demonstrate relevant qualifications and experience in both DevOps and machine learning.

In addition, the global MLOps market is projected to be worth more than $89.91 billion by 2034 (up from around $4.39 billion in 2026), which indicates increased MLOps job opportunities in the future [4]. Industries that rely heavily on machine learning are likely to see the most MLOps job growth, including:

• 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 [5]. According to Glassdoor, the median total pay for an MLOps engineer in the US is $161,000 annually, which includes a base pay of $106,000 to $149,000 and additional pay of $27,000 to $50,000 [1].

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 careers below 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 15 percent from 2024 to 2034, with an average of 129,200 new openings yearly [6]. Job growth for data scientists is expected to increase by 34 percent over the decade [7].

All salary information represents the median total pay from Glassdoor as of August 2026. These figures include base salary and additional pay, which may represent profit-sharing, commissions, bonuses, or other compensation.

1. Machine learning engineer

Median annual total salary in the US: $164,000 [8]

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 a similar field.

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

Median annual total salary in the US: $145,000 [9]

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)

Median annual total salary in the US: $173,000 [10]

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

Median annual total salary in the US: $157,000 [11]

Requirements: A bachelor’s degree in computer science, information technology, or a similar field may be required for a data scientist.

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.

Drive meaningful growth through accessible learning resources

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• Watch on YouTube: What Is MLOps? From Model to Production

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