Salary for Mid-level / Intermediate MLOps Engineer during 2024

Salary details

The average mid-level / intermediate MLOps Engineer salary lies between USD 114,800 and USD 175,000 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. 17, 2024

Salary trend

Top 20 Job Tags for Mid-level / Intermediate MLOps Engineer roles

The three most common job tag items assiciated with mid-level / intermediate MLOps Engineer job listings are MLOps, Machine Learning and ML models. 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 | 49 jobs Machine Learning | 48 jobs ML models | 41 jobs Python | 40 jobs Engineering | 40 jobs Pipelines | 34 jobs Computer Science | 30 jobs AWS | 29 jobs Azure | 27 jobs Kubernetes | 26 jobs Docker | 26 jobs GCP | 25 jobs DevOps | 25 jobs CI/CD | 25 jobs Testing | 22 jobs PyTorch | 19 jobs Model training | 18 jobs TensorFlow | 16 jobs MLFlow | 16 jobs Research | 15 jobs

Top 20 Job Perks/Benefits for Mid-level / Intermediate MLOps Engineer roles

The three most common job benefits and perks assiciated with mid-level / intermediate MLOps Engineer job listings are Career development, Health care and Competitive pay. 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 | 32 jobs Health care | 14 jobs Competitive pay | 14 jobs Flex hours | 12 jobs Startup environment | 12 jobs Salary bonus | 7 jobs Equity / stock options | 6 jobs Team events | 6 jobs Gear | 4 jobs Parental leave | 3 jobs Wellness | 2 jobs Insurance | 2 jobs 401(k) matching | 1 jobs Lunch / meals | 1 jobs Flex vacation | 1 jobs Transparency | 1 jobs Yoga | 1 jobs Medical leave | 1 jobs Home office stipend | 1 jobs Unlimited paid time off | 1 jobs

Salary Composition for a Mid-level MLOps Engineer

The salary for a Mid-level MLOps Engineer typically comprises a base salary, performance bonuses, and additional remuneration such as stock options or benefits. The base salary is the fixed component and usually forms the largest part of the total compensation package. Performance bonuses can vary significantly depending on the company's success and individual performance, often ranging from 10% to 20% of the base salary. Additional remuneration might include stock options, especially in tech companies or startups, and benefits like health insurance, retirement contributions, and paid time off.

Regional differences can impact salary composition. For instance, tech hubs like Silicon Valley or New York City might offer higher base salaries and more substantial stock options due to the high cost of living and competitive job market. In contrast, companies in smaller cities or regions might offer lower base salaries but compensate with other benefits. Industry also plays a role; tech companies might offer more in stock options, while finance or healthcare sectors might provide higher bonuses. Company size can influence the package as well, with larger companies often providing more comprehensive benefits and smaller startups offering equity as a significant part of the compensation.

Steps to Increase Salary from a Mid-level Position

To increase your salary from a mid-level MLOps Engineer position, consider the following strategies:

• Skill Enhancement: Continuously update your skills in the latest MLOps tools and technologies. Specializing in high-demand areas like cloud computing, container orchestration, or AI model deployment can make you more valuable.

• Advanced Education: Pursuing further education, such as a master's degree in data science or a related field, can open up higher-paying opportunities.

• Leadership Roles: Aim for leadership or managerial roles within your team. Demonstrating leadership skills and taking on more responsibilities can lead to promotions and salary increases.

• Networking: Engage with professional networks and communities. Networking can lead to opportunities in higher-paying companies or roles.

• Certifications: Obtain relevant certifications that can validate your expertise and potentially lead to salary negotiations.

Educational Requirements for a Mid-level MLOps Engineer

Most mid-level MLOps Engineer positions require at least a bachelor's degree in computer science, data science, engineering, or a related field. A strong foundation in programming, statistics, and machine learning is essential. Some positions might prefer candidates with a master's degree, especially for roles that involve more complex problem-solving or leadership responsibilities.

Helpful Certifications

Certifications can enhance your credibility and demonstrate your commitment to the field. Some valuable certifications for MLOps Engineers include:

• Certified Kubernetes Administrator (CKA): Demonstrates expertise in managing Kubernetes clusters, a crucial skill for MLOps.

• AWS Certified Machine Learning – Specialty: Validates your ability to design, implement, and maintain machine learning solutions on AWS.

• Google Professional Machine Learning Engineer: Shows proficiency in designing, building, and productionizing ML models on Google Cloud.

• Microsoft Certified: Azure AI Engineer Associate: Focuses on using Azure services to build and deploy AI solutions.

Experience Requirements

Typically, a mid-level MLOps Engineer should have 3-5 years of experience in related roles, such as software engineering, data engineering, or machine learning. Experience with cloud platforms (AWS, Azure, Google Cloud), containerization (Docker, Kubernetes), and CI/CD pipelines is often required. Familiarity with machine learning frameworks (TensorFlow, PyTorch) and programming languages (Python, R) is also essential.

Related salaries

Want to contribute?