MLOps Engineer Salary Trends and Job Market Analysis

As of 2022, the average salary for an MLOps Engineer in the United States is around $141,000 per year. This is a significant increase from 2020, when the average salary was around $114,000.

The demand for MLOps Engineers is skyrocketing, with the global market expected to grow by 30% by 2025. This growth is driven by the increasing adoption of artificial intelligence and machine learning in various industries.

According to Glassdoor, the highest paying cities for MLOps Engineers are San Francisco, New York, and Seattle, with average salaries ranging from $155,000 to $143,000 per year.

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An MLOps Engineer is a professional who specializes in the intersection of machine learning and DevOps. They focus on automating and scaling machine learning workflows.

Their primary goal is to get models into production quickly and efficiently, often working with data scientists and engineers to ensure seamless collaboration.

MLOps Engineers typically have a strong background in software engineering and data science, which enables them to bridge the gap between these two disciplines.

They are responsible for implementing and maintaining the infrastructure and tools needed to support machine learning workflows, such as model serving, monitoring, and versioning.

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Machine learning engineers are the backbone of the MLOps ecosystem, and understanding their role and responsibilities is essential to grasping the concept of an MLOps engineer.

Machine learning engineers design and develop algorithms, models, and systems that enable machines to learn from data and make predictions or decisions. Their work is crucial in various industries, including healthcare, finance, and technology.

To become a machine learning engineer, you typically need a strong foundation in programming, mathematics, and computer science. Many machine learning engineers also hold advanced degrees in these fields.

Machine learning engineers can expect varying salaries depending on their level of experience. Here's a breakdown of average salaries for machine learning engineers in the U.S.:

Senior machine learning engineers have the most expertise and experience, commanding higher salaries and often taking on leadership roles within their organizations.

MLOps refers to the combination of Machine Learning, DevOps, and Data Engineering to create a robust automation and tracking system for Machine Learning models.

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MLOps is deeply collaborative in nature, aiming to eliminate waste and automate as much as possible, producing richer and more consistent insights in large-scale production environments.

The job of an MLOps Engineer is highly paid, with companies willing to pay top-dollar for the best talent.

The average monthly salary for an MLOps Engineer varies across countries, but here's a quick comparison:

Note that these figures are net and do not include service fees.

The salary for MLOps Engineers can be quite impressive, with the average hourly rate surpassing the price of a fancy dinner for two - a whopping $59.69 per hour.

In the United States, the salary range for MLOps Engineers is between $110,000 and $180,000 per year, with senior engineers earning up to $200,000 annually.

The top 10% of MLOps Engineers can earn over $190,000 per year, which is a testament to the high demand for their skills in the industry.

Here's a breakdown of the salary ranges for MLOps Engineers in different countries:

MLOps Engineers with experience in multiple machine learning frameworks can command higher salaries, with some senior engineers earning up to $250,000 annually.

Experience and expertise are crucial factors influencing an ML engineer's salary. As professionals gain more experience in the field, their salaries tend to increase, reflecting their enhanced skills and value to employers.

Senior ML engineers with several years of experience, especially those who have led projects or teams, typically command higher salaries. In the United States, for example, the average salary for a machine learning engineer ranges from $116,387 to $160,568 per year.

Expertise in specific areas of machine learning can also lead to higher earnings. In Japan, the average annual salary for a machine learning engineer is JPY 10,830,085, with a range between JPY 7,451,098 and JPY 13,223,533, with salaries varying based on experience and qualifications.

Here's a breakdown of average salaries by experience level in different countries:

Experience and expertise are key factors that influence an ML engineer's salary. Professionals with more experience in the field tend to earn higher salaries, reflecting their enhanced skills and value to employers.

Senior ML engineers with several years of experience, especially those who have led projects or teams, command higher salaries. They are often sought after for their leadership skills and ability to manage complex projects.

Expertise in specific areas of machine learning can also lead to higher earnings. ML engineers with advanced skills in deep learning, computer vision, natural language processing, or generative AI are often in higher demand and can negotiate better compensation.

Gaining practical experience is another effective way to boost your salary as an ML engineer. Theoretical knowledge is important, but being able to apply that knowledge to solve real-world problems is what truly sets you apart.

Seeking out opportunities for practical experience, such as through internships or personal projects, can significantly boost your resume and problem-solving skills. This experience allows you to develop critical skills that are essential in the machine learning field.

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Looking at the salary histogram for jobs citing MLOps, we can see that the distribution is quite broad. The highest salary range is $150,000-$200,000.

It's worth noting that the median salary for MLOps professionals is around $120,000. This suggests that while some MLOps professionals can earn very high salaries, the majority fall in a more moderate range.

The salary distribution for MLOps jobs is likely influenced by factors such as location, industry, and level of experience. For example, professionals working in the finance industry may earn higher salaries than those in non-profit sectors.

As an ML engineer, your career development and growth can significantly impact your salary. Experience and expertise are crucial factors influencing your earning potential, with senior ML engineers commanding higher salaries.

Gaining experience in the field can lead to increased salaries, with professionals who have led projects or teams typically earning more. In fact, senior ML engineers with several years of experience can negotiate better compensation.

Continuously upgrading your skills and staying abreast of the latest developments in machine learning is also essential. This can make you more attractive to potential employers and lead to higher-paying positions.

Networking is key, with 85% of jobs being filled through connections, not job boards.

Having a mentor can make a huge difference, with mentees experiencing a 35% increase in career advancement.

Developing a growth mindset is essential, as it allows you to learn from failures and adapt to new situations.

Regular self-assessment is crucial, with 75% of professionals identifying a lack of clear goals as a major obstacle to career advancement.

Continuously updating your skills is vital, with 60% of employers citing a lack of relevant skills as a reason for not hiring a candidate.

Gaining practical experience is crucial for career growth as an ML engineer. This is because theoretical knowledge is important, but being able to apply that knowledge to solve real-world problems is what truly sets you apart.

Seek out opportunities for practical experience through your job, internships, or personal projects. Practical experience not only boosts your resume but also allows you to develop problem-solving skills that are critical in the machine learning field.

Practical experience is a key differentiator in the job market. It shows that you're not just book-smart, but also able to apply your knowledge to solve real-world problems.

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The job market for MLOps engineers is a rapidly growing field, with a significant increase in job postings citing MLOps as a proportion of all IT jobs advertised.

According to recent data, MLOps job postings have been on the rise, indicating a growing demand for professionals with these skills.

In terms of salary, MLOps engineers can expect to earn a competitive income. Here's a breakdown of average salaries for MLOps engineers in different countries:

The growth of MLOps is predicted to continue, with estimates suggesting it will reach up to $4.5 billion by the end of 2025, indicating a bright future for professionals in this field.

Industry and Sector is a crucial factor in determining an ML engineer's earnings.

ML engineers working in high-value industries like finance, healthcare, and technology tend to have higher salaries.

These industries place a high value on machine learning, making ML engineers a valuable asset.

In contrast, ML engineers working in academia, government, or non-profit sectors may experience lower salary ranges due to tighter budget constraints.

However, these roles can offer other forms of compensation, such as greater job security or more flexible work hours.

Finance, tech giants like Google, Amazon, or Facebook, and other high-value industries often offer the highest salaries to ML engineers.

These companies recognize the importance of machine learning in their business and are willing to pay top dollar for talented engineers.

Job vacancy trends for MLOps professionals are on the rise. According to job postings, MLOps is increasingly being cited as a required skill.

As a result, the number of job postings citing MLOps as a proportion of all IT jobs advertised is growing. This trend is expected to continue in the coming years.

Here's a rough idea of the current job market:

The growing demand for MLOps professionals is driven by the increasing importance of this practice in organizations. As data and technology continue to expand, the need for MLOps experts will only continue to rise.

The job market for MLOps professionals is quite interesting, and if you're considering a career in this field, you'll want to know where the top job locations are. England has seen a significant decline in demand, with a rank change of -79 over the past year, but still offers a median salary of £90,000.

London, on the other hand, has seen a more modest decline in demand, with a rank change of -18, but boasts a median salary of £100,000. This is a 23.08% increase from the same period last year.

The North of England has seen a slight increase in demand, with a rank change of +1, and offers a median salary of £69,000.

Here's a breakdown of the top job locations for MLOps professionals in the UK:

Work from Home is also a notable location, with a median salary of £75,000, but has seen a decline in demand, with a rank change of -3.

Uber took machine learning to scale by using it for various applications like estimating meal arrival times and anticipating demand for drivers. They realized that having the technology wasn't enough, and effective collaboration among teams was necessary.

Uber's solution was to create Uber Michelangelo, a machine learning platform that standardized workflow across teams. This helped them properly use machine learning at scale.

Booking.com has around 150 distinct machine learning models in use, which were built and deployed using an iterative, hypothesis-driven process integrated with other disciplines.

Cevo created an automated machine learning pipeline for a banking customer, cutting the time it took to train and deploy machine learning models from months to days. A model capable of recognizing new types of fraud every month was created in under three hours.

Here are some examples of companies using machine learning operations (MLOps) effectively:

DevOpsSchool offers a Certified MLOps Engineer Training Course, which is a completely online course that takes approximately 35 hours to complete.

Curious to learn more? Check out: Mlops Training

This course is designed to teach the best practices for deploying, evaluating, monitoring, and operating production ML systems on AWS Cloud.

Continuous learning is crucial in the rapidly evolving field of machine learning, and certification can demonstrate your commitment to staying up-to-date with new techniques and best practices.

Several reputable institutions offer machine learning certification programs that can validate your knowledge and skills, making you more attractive to employers and clients.

The Certified MLOps Engineer Training Course covers the subject of deploying Machine Learning models in production and at scale, and teaches the best MLOps tools, techniques, and practices for deploying, evaluating, monitoring, and operating production ML systems end-to-end.

By completing this course, you'll learn to deploy models into production environments using cutting-edge open-source frameworks.

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As an MLOps engineer, you'll often find yourself playing multiple roles, including data engineer, DevOps engineer, and machine learning engineer.

Your salary will vary depending on the specific role you're hired for, with data engineer roles typically paying around $140,000 per year.

You'll need to have a broad range of skills to be successful in these roles, including proficiency in languages like Python and R, as well as experience with cloud platforms like AWS and Azure.

The average salary for an MLOps engineer is around $170,000 per year, with the highest paying roles typically found in finance and healthcare.

Machine Learning is the most frequently mentioned co-occurring skill with MLOps, appearing in 88.42% of job ads.

Artificial Intelligence is a close second, appearing in 80% of job ads. This makes sense, given the close relationship between AI and MLOps.

Python is a popular programming language in the MLOps field, appearing in 72.63% of job ads.

Data Science is another key area, appearing in 67.37% of job ads.

The following skills and capabilities are also frequently mentioned in job ads for MLOps:

These skills and capabilities are not only important for MLOps, but also for related fields like Data Science and Artificial Intelligence.

Data Scientist vs. DevOps Engineer is a crucial distinction in the machine learning ecosystem. The entire lifecycle of a machine learning system is tightly intertwined and highly iterative, requiring expertise from both Data Science and DevOps.

Data Scientists focus on developing and training machine learning models, whereas DevOps Engineers are responsible for ensuring the smooth operation of these systems in production.

In the context of machine learning, Data Scientists work on the development side, while DevOps Engineers work on the deployment and maintenance side.

Additional reading: Mlops vs Devops

As an MLOps engineer, your role is a unique blend of data scientist and DevOps expertise. You'll oversee ML model production, testing, and code optimization to ensure low latency.

Your primary responsibility is to ensure that ML models can handle large amounts of data at record-breaking speeds. This requires a deep understanding of MLOps architecture and automation technologies.

MLOps engineers need strong software programming skills, a strong command of MLOps architecture, and knowledge of ML automation technologies. They should also be well-versed in MLOps engineering on AWS, Azure, and GCP cloud solutions.

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A good MLOps engineer should be able to develop MLOps pipelines, have a deep understanding of Linux, and knowledge of frameworks such as Tensorflow, Keras, and PyTorch. They should also have experience in using leading MLOps frameworks like MLFlow, DataRobot, and Kubeflow.

Here are some essential technical skills required to become an MLOps developer: