What Is an MLOps Engineer?
MLOps, short for machine learning operations, is a practice that aims to unify machine learning (ML) system development and ML system operations. The main goal of MLOps is to shorten the lead time between developing a model and getting it into production, while ensuring the model's quality.
An MLOps engineer is a professional who oversees the deployment and maintenance of machine learning models. They are responsible for developing infrastructure and tools that allow for the rapid deployment and seamless scaling of these models. These engineers work closely with data scientists, ensuring that models are effective within the operational environment.
MLOps engineers play a critical role in the field of data science and machine learning. Their role is essential in ensuring the smooth operation of machine learning models, which in turn significantly impacts the decision-making processes of businesses and organizations.
This is part of a series of articles about machine learning engineering
In this article:
MLOps Engineer Salaries Across the World
The numbers in the table below were taken from salary surveys, published in late 2023 in the respective countries. The numbers are only averages and may not be representative of all MLOps engineers in the respective countries.
Factors Influencing MLOps Engineer Salaries
Education and Certifications
A solid educational foundation in computer science, data science, or a related field is highly valued in the industry. A bachelor's or master's degree can provide you with the essential knowledge and skills needed to excel in the field of MLOps.
In addition to formal education, certifications can also play a crucial role in boosting your salary as an MLOps engineer. Certifications such as the AWS Certified Machine Learning—Specialty or the Google Cloud Certified—Professional Data Engineer demonstrate your expertise in specific platforms and technologies. These certifications not only validate your skills but also enhance your credibility in the eyes of potential employers, resulting in higher salary offers.
Experience Level
Early in your career, you may start as an entry-level MLOps engineer, working under the guidance of more experienced professionals. However, as you gain hands-on experience and demonstrate your ability to handle complex projects, you can progress to more senior roles with increased responsibilities and higher salaries.
To advance your career and command a higher salary, it's essential to continually seek opportunities to broaden your experience. This can include working on diverse projects, collaborating with cross-functional teams, and taking on leadership roles within your organization.
Geographic Location
Just like in many other professions, the geographic location can significantly impact the salary of an MLOps engineer. Salaries can vary significantly depending on the cost of living and demand for MLOps professionals in a particular region.
In general, cities with a thriving tech industry and a high concentration of technology companies tend to offer higher salaries for MLOps engineers. For example, major tech hubs like San Francisco, New York, and Seattle often provide more lucrative opportunities compared to smaller cities or rural areas.
It’s important to consider that remote work opportunities have become more prevalent in recent years, allowing MLOps engineers to work for companies located in high-paying regions while enjoying a lower cost of living elsewhere.
Industry Demand
The demand for MLOps engineers can vary across different industries, and this can have a significant impact on salary levels. Industries that heavily rely on machine learning and artificial intelligence, such as finance, healthcare, and eCommerce, often offer higher salaries to attract top MLOps talent.
When considering career opportunities, it can be beneficial to research industries that are experiencing rapid growth and investment in machine learning technologies. By targeting industries with a high demand for MLOps engineers, you can increase your chances of securing a well-paying job.
Related content: Read our guide to machine learning workflow
Tips for Improving Your MLOps Engineer Salary
Now that we've explored the factors that can influence your MLOps engineer salary, let's discuss some actionable tips to improve your earning potential in this field.
Deepen Technical Expertise
As an MLOps engineer, having a deep technical expertise is crucial for commanding a higher salary. Stay updated with the latest tools, frameworks, and technologies used in MLOps and invest time in mastering them.
This can include learning popular machine learning platforms like TensorFlow or PyTorch, understanding cloud computing technologies such as Amazon Web Services (AWS) or Google Cloud Platform (GCP), and becoming proficient in programming languages such as Python or R. By continuously expanding your technical knowledge and skills, you can position yourself as a valuable asset to employers, increasing your market value and salary potential.
Acquire Relevant Certifications
In addition to broadening your technical expertise, acquiring relevant certifications can also significantly enhance your earning potential as an MLOps engineer. Certifications validate your skills and demonstrate your commitment to professional growth.
Consider pursuing certifications that align with the specific platforms and technologies used in MLOps. For example, certifications like the Microsoft Certified: Azure AI Engineer Associate or the IBM Certified Data Engineer can showcase your proficiency in respective platforms and technologies.
Showcase Your Work
Building a strong portfolio of projects and showcasing your work can be a powerful tool for boosting your MLOps engineer salary. Employers want to see tangible evidence of your skills and ability to deliver results.
Take on side projects or participate in open-source initiatives to gain practical experience in deploying and managing machine learning models. Document your work and highlight the impact it had on improving efficiency, reducing costs, or enhancing business outcomes.
Stay Updated with Industry Trends
The field of MLOps is constantly evolving, with new tools, techniques, and best practices emerging regularly. To remain competitive and maximize your earning potential, it's crucial to stay updated with the latest industry trends.
Subscribe to industry newsletters, join online communities, and attend conferences and meetups to stay connected with the MLOps community. Engage in discussions and share knowledge with peers to deepen your understanding of emerging technologies and trends.
By staying informed about the latest advancements in MLOps, you can position yourself as a forward-thinking professional, making you more valuable to potential employers and increasing your salary prospects.
Stay Ahead of the ML Curve with Run:ai
In today’s highly competitive economy, enterprises are looking to Artificial Intelligence in general and Machine and Deep Learning in particular to transform big data into actionable insights that can help them better address their target audiences, improve their decision-making processes, and streamline their supply chains and production processes, to mention just a few of the many use cases out there. In order to stay ahead of the curve and capture the full value of ML, however, companies must strategically embrace MLOps.
Run:ai’s AI/ML virtualization platform is an important enabler for Machine Learning Operations teams. Focusing on deep learning neural network models that are particularly compute-intensive, Run:ai creates a pool of shared GPU and other compute resources that are provisioned dynamically to meet the needs of jobs in process. By abstracting workloads from the underlying infrastructure, organizations can embrace MLOps and allow data scientists to focus on models, while letting IT teams gain control and real-time visibility of compute resources across multiple sites, both on-premises and in the cloud.
See for yourself how Run:ai can operationalize your data science projects, accelerating their journey from research to production.
