MLOps Engineer Salary in 2023

Salary details

The average MLOps Engineer salary lies between USD 73,100 and USD 160,000 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 MLOps Engineer roles

The three most common job tag items assiciated with MLOps Engineer job listings are MLOps, Machine Learning 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:

MLOps | 103 jobs Machine Learning | 102 jobs Python | 85 jobs Engineering | 74 jobs Pipelines | 73 jobs Kubernetes | 65 jobs AWS | 57 jobs CI/CD | 56 jobs Docker | 55 jobs ML models | 52 jobs DevOps | 52 jobs TensorFlow | 47 jobs Testing | 42 jobs Azure | 42 jobs Computer Science | 42 jobs PyTorch | 41 jobs GCP | 41 jobs MLFlow | 39 jobs Terraform | 36 jobs Kubeflow | 32 jobs

Top 20 Job Perks/Benefits for MLOps Engineer roles

The three most common job benefits and perks assiciated with MLOps Engineer job listings are Career development, Flex hours and Health care. 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 | 73 jobs Flex hours | 35 jobs Health care | 35 jobs Flex vacation | 34 jobs Parental leave | 25 jobs Startup environment | 23 jobs Team events | 23 jobs Equity / stock options | 22 jobs Competitive pay | 20 jobs Salary bonus | 15 jobs Medical leave | 14 jobs Conferences | 10 jobs Insurance | 10 jobs 401(k) matching | 7 jobs Unlimited paid time off | 7 jobs Transparency | 5 jobs Relocation support | 5 jobs Pet friendly | 5 jobs Home office stipend | 5 jobs Gear | 4 jobs

Salary Composition for MLOps Engineers

The salary composition for MLOps Engineers can vary significantly based on factors such as region, industry, and company size. Typically, the salary is divided into three main components: a fixed base salary, a performance-based bonus, and additional remuneration such as stock options or benefits.

• Region: In tech hubs like Silicon Valley, New York, or Seattle, the base salary tends to be higher due to the cost of living and competitive job market. In contrast, regions with a lower cost of living may offer a smaller base salary but could compensate with other benefits.
• Industry: Industries such as finance, healthcare, and technology often offer higher salaries due to the critical nature of data and AI in their operations. Conversely, non-profit or educational sectors might offer lower salaries but could provide other forms of compensation like flexible working conditions.
• Company Size: Larger companies often have more resources to offer competitive salaries, bonuses, and stock options. Startups might offer lower base salaries but compensate with equity or stock options, which could be lucrative if the company succeeds.

Steps to Increase Salary

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

• Skill Enhancement: Continuously update your skills in the latest AI/ML tools and technologies. Specializing in niche areas like deep learning, natural language processing, or cloud-based MLOps can make you more valuable.
• Advanced Education: Pursuing a master's degree or Ph.D. in a related field can open up higher-paying opportunities and leadership roles.
• Networking: Engage with professional networks and communities. Attending conferences, webinars, and meetups can lead to new opportunities and insights into higher-paying roles.
• Certifications: Obtain relevant certifications that can validate your skills and make you stand out in the job market.
• Leadership Roles: Aim for leadership or managerial positions within your organization, which typically come with higher salaries.

Educational Requirements

Most MLOps Engineer positions require at least a bachelor's degree in computer science, data science, engineering, or a related field. However, many employers prefer candidates with a master's degree or higher, especially for senior roles. A strong foundation in mathematics, statistics, and programming is essential, as is knowledge of machine learning algorithms and data management.

Helpful Certifications

Certifications can be a great way to demonstrate your expertise and commitment to the field. Some valuable certifications for MLOps Engineers include:

• Google Cloud Professional Machine Learning Engineer
• AWS Certified Machine Learning – Specialty
• Microsoft Certified: Azure AI Engineer Associate
• Certified Kubernetes Administrator (CKA)
• TensorFlow Developer Certificate

These certifications can help validate your skills in specific platforms and tools, making you more attractive to potential employers.

Required Experience

Typically, MLOps Engineer roles require 3-5 years of experience in related fields such as software engineering, data engineering, or data science. Experience with cloud platforms (AWS, Google Cloud, Azure), containerization (Docker, Kubernetes), and CI/CD pipelines is often essential. Familiarity with machine learning frameworks like TensorFlow, PyTorch, or Scikit-learn is also commonly required.

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