Salary for Senior-level / Expert MLOps Engineer during 2024

Salary details

The average senior-level / expert MLOps Engineer salary lies between USD 110,000 and USD 210,000 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: Nov. 24, 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 MLOps, Machine Learning and Python. Below you find a list of the 20 most occuring job tags in 2024 and the number of open jobs that where associated with them during that period:

MLOps | 227 jobs Machine Learning | 223 jobs Python | 193 jobs Pipelines | 189 jobs Engineering | 183 jobs Kubernetes | 164 jobs CI/CD | 155 jobs ML models | 154 jobs Docker | 142 jobs AWS | 137 jobs DevOps | 123 jobs PyTorch | 112 jobs TensorFlow | 106 jobs Testing | 103 jobs Azure | 102 jobs Computer Science | 91 jobs GCP | 90 jobs Architecture | 86 jobs MLFlow | 85 jobs Kubeflow | 83 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, Health care and Flex hours. Below you find a list of the 20 most occuring job perks or benefits in 2024 and the number of open jobs that where offering them during that period:

Career development | 168 jobs Health care | 76 jobs Flex hours | 66 jobs Equity / stock options | 55 jobs Competitive pay | 55 jobs Startup environment | 53 jobs Team events | 52 jobs Flex vacation | 47 jobs Parental leave | 32 jobs Insurance | 31 jobs Medical leave | 25 jobs Salary bonus | 21 jobs Wellness | 18 jobs 401(k) matching | 16 jobs Transparency | 16 jobs Home office stipend | 14 jobs Gear | 13 jobs Relocation support | 13 jobs Conferences | 12 jobs Unlimited paid time off | 9 jobs

Salary Composition for a Senior MLOps Engineer

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 base salary is often the largest component, accounting for 70-80% of the total compensation package. Bonuses can vary significantly based on company performance and individual contributions, usually ranging from 10-20% of the base salary. Additional remuneration, such as stock options, can be a significant part of the package in startups or large tech firms, potentially adding another 10-20% to the total compensation.

Regional differences also play a role; for instance, salaries in tech hubs like San Francisco or New York tend to be higher due to the cost of living and competitive job market. Industry can also influence salary composition, with finance and tech industries typically offering higher compensation packages compared to others. Company size can affect the availability and size of bonuses and stock options, with larger companies often providing more substantial additional remuneration.

Steps to Increase Salary Further

To increase your salary beyond the median for a Senior MLOps Engineer, consider the following strategies:

• Specialize in Niche Areas: Developing expertise in niche areas such as AI ethics, edge computing, or quantum machine learning can make you more valuable.
• Leadership Roles: Transitioning into leadership or managerial roles can significantly boost your salary.
• Continuous Learning: Stay updated with the latest technologies and methodologies in AI/ML and MLOps.
• Networking: Build a strong professional network to learn about higher-paying opportunities.
• Negotiation Skills: Improve your negotiation skills to better advocate for higher compensation during job offers or performance reviews.

Educational Requirements

Most Senior MLOps Engineer positions require at least a bachelor's degree in computer science, engineering, mathematics, or a related field. However, a master's degree or Ph.D. can be advantageous and sometimes preferred, especially for roles in research-intensive industries or companies. Advanced degrees can provide a deeper understanding of machine learning algorithms, data structures, and software engineering principles, which are crucial for this role.

Helpful Certifications

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

• Certified Kubernetes Administrator (CKA): Useful for managing containerized applications.
• AWS Certified Machine Learning – Specialty: Demonstrates proficiency in using AWS for machine learning projects.
• Google Professional Machine Learning Engineer: Validates your ability to design, build, and productionize ML models on Google Cloud.
• TensorFlow Developer Certificate: Shows proficiency in using TensorFlow for building ML models.

Required Experience

Typically, a Senior MLOps Engineer is expected to have 5-10 years of experience in software engineering, data science, or a related field. This experience should include a strong background in deploying and managing machine learning models in production environments. Experience with cloud platforms, containerization, CI/CD pipelines, and infrastructure as code is also highly valued.

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