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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<\/p>\n<\/li>\n<\/ul>\n
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MLOps engineer jobs require technical knowledge of machine learning algorithms, DevOps, data science, workflow automation, programming languages, and Agile software development<\/p>\n<\/li>\n<\/ul>\n
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An MLOps engineer\u2019s salary is $161,000 annually, representing the median total pay in the US [1]<\/p>\n<\/li>\n<\/ul>\n
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 reties, and how to pursue this up-and-coming career<\/p>\n
If you\u2019re ready to start preparing for an MLOps role, enroll in the Machine Learning Specialization from Stanford University and DeepLearning.AI. You\u2019ll have the opportunity to learn fundamental machine learning concepts and how to apply them to real-world AI applications in as little as two months. Upon completion, you\u2019ll have earned a career certificate for your<\/a> resume<\/p>\n
What is MLOps?<\/h2>\n
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<\/p>\n
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.<\/p>\n
What is an MLOps engineer?<\/h2>\n
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<\/p>\n
MLOps vs. DevOps<\/h3>\n
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<\/p>\n
Is MLOps harder than DevOps?<\/h3>\n
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<\/p>\n
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.<\/p>\n
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<\/p>\nWhat does an MLOps engineer do?<\/h2>\n
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:<\/p>\n
Development:<\/b><\/p>\n
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Overseeing the ML model pipeline<\/p>\n<\/li>\n
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Approving changes and reviewing features<\/p>\n<\/li>\n
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Monitoring the success of testing<\/p>\n<\/li>\n
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Ensuring model artifacts are properly handled<\/p>\n<\/li>\n<\/ul>\n
Deployment:<\/b><\/p>\n
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Training and testing ML models<\/p>\n<\/li>\n<\/ul>\n
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Using continuous integration\/continuous deployment (CI\/CD) techniques<\/p>\n<\/li>\n<\/ul>\n
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Using tools like Docker and Kubernetes to deploy ML models to production<\/p>\n<\/li>\n<\/ul>\n
Management and monitoring:<\/b><\/p>\n
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Monitoring data, creating reports, and necessary documents<\/p>\n<\/li>\n<\/ul>\n
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Implementing automated model retraining functions<\/p>\n<\/li>\n<\/ul>\n
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Using monitoring tools to track error rates, response times, and re<\/p>\n<\/li>\n<\/ul>\n
MLOps engineer vs. ML engineer<\/h3>\n
As an MLOps engineer, you\u2019re responsible for machine learning models’ workflows and life cycles to get them to production. This differs from an ML engineer role, where you\u2019re 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<\/p>\n
Skills for MLOps engineer jobs<\/h2>\n
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<\/p>\n
Technical skills:<\/b><\/p>\n
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Machine learning algorithms<\/p>\n<\/li>\n
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DevOps<\/p>\n<\/li>\n
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Data science<\/p>\n<\/li>\n
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Automating workflows<\/p>\n<\/li>\n
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CI\/CD<\/p>\n<\/li>\n
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Software development<\/p>\n<\/li>\n
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Agile methodologies<\/p>\n<\/li>\n
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Programming languages: Python, C++, Java<\/p>\n<\/li>\n
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Software testing<\/p>\n<\/li>\n
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Statistical modeling<\/p>\n<\/li>\n
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Database construction and administration: SQL<\/p>\n<\/li>\n<\/ul>\n
Workplace skills:<\/b><\/p>\n
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Collaboration<\/p>\n<\/li>\n
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Communication<\/p>\n<\/li>\n
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Organization<\/p>\n<\/li>\n<\/ul>\n
Job outlook for MLOps engineers<\/h2>\n
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\u2019s AI\/ML competencies as the lowest among 30 skills [2]<\/p>\n
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<\/p>\n
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<\/a>]. Industries that rely heavily on machine learning are likely to see the most MLOps job growth, including:<\/p>\n
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Banking<\/p>\n<\/li>\n
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Health care<\/p>\n<\/li>\n
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Manufacturing<\/p>\n<\/li>\n
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Marketing and sales<\/p>\n<\/li>\n
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Retail<\/p>\n<\/li>\n<\/ul>\n
MLOps engineer salary<\/h2>\n
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]<\/p>\n
How to become an MLOps engineer<\/h2>\n
As MLOps is such a new field, you don\u2019t 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\u2019ll generally need a bachelor\u2019s degree in a relevant major, such as computer science, data science, software engineering, math, or statistics, along with some related experience in the field.<\/p>\n
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<\/p>\n
Similar careers to an MLOps engineer<\/h2>\n
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<\/p>\n
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]<\/p>\n
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<\/p>\n
1. Machine learning engineer<\/h3>\n
Median annual total salary in the US:<\/b> $164,000 [8<\/a>]<\/p>\n
Requirements: As a machine learning engineer, you may need a bachelor\u2019s degree and a master\u2019s degree in data science, software engineering, electrical engineering, computer science, or a similar field<\/p>\n
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<\/p>\n
2. DevOps engineer<\/h3>\n
Median annual total salary in the US:<\/b> $145,000 [9<\/a>]<\/p>\n
Requirements:You may need a bachelor\u2019s degree in computer science or a similar field as a DevOps engineer. You may also consider pursuing a master\u2019s degree for career progression<\/p>\n
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<\/p>\n
3. Site reliability engineer (SRE)<\/h3>\n
Median annual total salary in the US:<\/b> $173,000 [10<\/a>]<\/p>\n
Requirements: As a site reliability engineer, you may need a bachelor\u2019s degree in computer science, software design, computer engineering, or a similar field. Some employers expect a master\u2019s degree<\/p>\n
As an SRE, you would enhance a system\u2019s 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<\/p>\n
4. Data scientist<\/h3>\n
Median annual total salary in the US:<\/b>$157,000 [11<\/a>]<\/p>\n
Requirements: A bachelor\u2019s degree in computer science, information technology, or a similar field may be required for a data scientist<\/p>\n
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<\/p>\n
Drive meaningful growth through accessible learning resources<\/h2>\n
Subscribe to Career Chaton LinkedIn to stay current with the latest trends in your career field. Continue your learning journey in MLOps and AI with our other free digital re<\/p>\n
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Structure your study:<\/b>MLOps Learning Roadmap: From Beginner to Expert<\/p>\n<\/li>\n<\/ul>\n
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Watch on YouTube:<\/b>What Is MLOps? From Model to Production<\/a><\/p>\n<\/li>\n<\/ul>\n
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Learn key concepts:<\/b>Artificial Intelligence Glossary: Learn AI Vocabulary<\/p>\n<\/li>\n<\/ul>\n
Whether you want to develop a new skill, get comfortable with an in-demand technology, or advance your abilities, keep growing with a Coursera Plus subscription. You\u2019ll get access to over 10,000 flexible courses<\/p>\n
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<\/li>\n<\/ul>\nArticle sources<\/h2>\n
1.\u00a0<\/p>\n
Glassdoor. \u201cMLOps Engineer Salaries, https:\/\/www.glassdoor.com\/Salaries\/mlops-engineer-salary-SRCH_KO0,14.htm.\u201d Accessed August 17, 2026<\/p>\n
Keep reading<\/h2>\n
- \n
- What Is the Best Platform for MLOps? Your Guide to MLOps Software
\nAugust 26, 2025<\/li>\n - Machine Learning Engineer vs. Data Scientist: What\u2019s the Difference?
\nJanuary 31, 2026<\/li>\n - Machine Learning Engineer Salary: How Much Can You Make?
\nAugust 20, 2026<\/li>\n - Machine Learning Skills: Your Guide to Getting Started
\nNovember 29, 2025<\/li>\n - 7 Machine Learning Roles and How to Get Started
\nApril 18, 2026<\/li>\n - What Is AIOps? Definition, Examples, and Use Cases
\nOctober 14, 2025<\/li>\n<\/ul>\nUpdated on Aug 25, 2026
\nWritten by:<\/p>\nCoursera Staff<\/h3>\n
Editorial Team<\/p>\n
Coursera\u2019s editorial team is comprised of highly experienced professional editors, writers, and fact<\/p>\n
This content has been made available for informational purposes only. Learners are advised to conduct additional research to ensure that courses and other credentials pursued meet their personal, professional, and financial goals<\/p>\n
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- What Is the Best Platform for MLOps? Your Guide to MLOps Software
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