Salary for Senior-level / Expert MLOps Engineer during 2023

Salary details

The average senior-level / expert MLOps Engineer salary lies between USD 139,700 and USD 199,000 globally. It represents the overall compensation/gross salary amount for the working year (before deductions like social security, taxes and other contributions), not including equity/stock options or similar benefits.

All data shown are full-time equivalent (FTE) salaries. Part-time salary information has been extrapolated to its FTE value.

Last updated: Dec. 22, 2024

Salary trend

Top 20 Job Tags for Senior-level / Expert MLOps Engineer roles

The three most common job tag items assiciated with senior-level / expert MLOps Engineer job listings are Machine Learning, MLOps and Python. Below you find a list of the 20 most occuring job tags in 2023 and the number of open jobs that where associated with them during that period:

Machine Learning | 68 jobs MLOps | 68 jobs Python | 56 jobs Pipelines | 52 jobs Engineering | 49 jobs Kubernetes | 47 jobs Docker | 42 jobs AWS | 39 jobs DevOps | 35 jobs ML models | 34 jobs GCP | 31 jobs CI/CD | 31 jobs Azure | 28 jobs TensorFlow | 27 jobs MLFlow | 27 jobs Spark | 25 jobs Testing | 25 jobs Terraform | 25 jobs Computer Science | 24 jobs Kubeflow | 23 jobs

Top 20 Job Perks/Benefits for Senior-level / Expert MLOps Engineer roles

The three most common job benefits and perks assiciated with senior-level / expert MLOps Engineer job listings are Career development, Flex hours and Flex vacation. Below you find a list of the 20 most occuring job perks or benefits in 2023 and the number of open jobs that where offering them during that period:

Career development | 43 jobs Flex hours | 25 jobs Flex vacation | 24 jobs Health care | 21 jobs Equity / stock options | 19 jobs Parental leave | 17 jobs Team events | 15 jobs Competitive pay | 14 jobs Salary bonus | 13 jobs Startup environment | 11 jobs Medical leave | 10 jobs Insurance | 8 jobs 401(k) matching | 6 jobs Conferences | 6 jobs Home office stipend | 5 jobs Unlimited paid time off | 5 jobs Transparency | 4 jobs Lunch / meals | 3 jobs Fitness / gym | 3 jobs Relocation support | 3 jobs

Salary Composition for Senior MLOps Engineer Roles

The salary for a Senior MLOps Engineer typically comprises a base salary, performance bonuses, and additional remuneration such as stock options or equity, especially in tech companies. The composition can vary significantly based on region, industry, and company size. In the United States, for instance, the base salary might constitute 70-80% of the total compensation package, with bonuses and equity making up the rest. In tech hubs like Silicon Valley, equity can be a substantial part of the package, whereas in other regions, cash bonuses might be more prevalent. In larger companies, you might find more structured bonus programs, while startups might offer more equity to compensate for lower base salaries.

Steps to Increase Salary from a Senior MLOps Position

To increase your salary from a Senior MLOps Engineer position, consider the following strategies:

• Specialize Further: Develop expertise in niche areas of MLOps, such as model deployment, monitoring, or data pipeline optimization, which can make you indispensable.
• Leadership Roles: Transition into leadership or managerial roles, such as MLOps Team Lead or Director of MLOps, which typically come with higher pay.
• Industry Switch: Consider moving to industries that pay higher salaries for MLOps roles, such as finance, healthcare, or tech giants.
• Continuous Learning: Stay updated with the latest tools and technologies in MLOps, and consider obtaining advanced certifications or degrees.
• Networking: Engage with professional networks and communities to learn about higher-paying opportunities and negotiate better offers.

Educational Requirements for Senior MLOps Engineers

Most Senior MLOps Engineer positions require at least a bachelor's degree in computer science, engineering, data science, or a related field. However, a master's degree or Ph.D. can be advantageous, especially for roles in research-intensive industries or companies. A strong foundation in software engineering, data structures, algorithms, and machine learning principles is essential.

Helpful Certifications for MLOps Engineers

While not always mandatory, certain certifications can enhance your profile and demonstrate your expertise:

• Certified Kubernetes Administrator (CKA): Useful for managing containerized applications.
• AWS Certified Machine Learning – Specialty: Demonstrates proficiency in deploying machine learning models on AWS.
• Google Professional Machine Learning Engineer: Validates skills in designing, building, and productionizing ML models on Google Cloud.
• Microsoft Certified: Azure AI Engineer Associate: Focuses on AI solutions on Microsoft Azure.

Experience Required for Senior MLOps Roles

Typically, a Senior MLOps Engineer is expected to have 5-10 years of experience in software engineering, data engineering, or related fields, with at least 2-3 years specifically in MLOps. Experience with cloud platforms (AWS, Azure, GCP), containerization (Docker, Kubernetes), CI/CD pipelines, and machine learning frameworks (TensorFlow, PyTorch) is often required. Demonstrated experience in deploying and maintaining machine learning models in production environments is crucial.

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