Navigating MLOps Engineers Salaries and Expertise: All You Need to Know

Today, AI and machine learning applications have become a key component in organizations, with more and more businesses looking for systematic ways to automate and implement their ML models. True to this, a recent report by Global Information shows that the Machine Learning technologies market is expected to hit the 30.45 billion mark by 2032, growing at a staggering CAGR of 40% during this decade — which is mind-boggling considering the technology is relatively new.

As businesses increasingly rely on machine learning, many of them face challenges related to the deployment and management of machine learning models, including their efficiency, scalability, automation, and accuracy. To address these challenges, Machine Learning and Operations are used to not only streamline workflows but also ensure continuous model improvement — all while enhancing collaboration between data scientists, software developers, data engineers, and other members of IT teams.

MLOps efficiency is impossible without the experts who drive it forward — MLOps-skilled engineers. So, what exactly do they do, and how do they differ from other related roles, such as data science experts and DevOps specialists? In this article, we’ll cover it all: their unique skill sets, and how they can contribute to your business goals and growth.

The Uniqueness of MLOps Engineer Job Role vs. Data Scientist vs. DevOps Engineer

Delving into the impact of Machine Learning and Operations, it plays a crucial role in the data-focused roles ecosystem — by ensuring that ML-powered models and algorithms are not only built but also effectively integrated, monitored, and maintained in production environments. Thus, it bridges the gap between machine learning development and real-world application — as illustrated in the image below.

Evidently, MLOps closely intersects with Machine Learning, DevOps, and Data Science/Engineering. But how do these roles differ, and how does it combine elements from each? Let’s explore this in detail.

Data Scientist Roles and Responsibilities

Right off the gate, a data science specialist can be described as a business-focused engineer who analyzes, identifies, and solves problems within Machine Learning algorithms. For example, if you’re looking to hire data scientists, you’ll land with a specialist capable of streamlining your organization’s processes as solutions to real business problems rather than focusing on object-oriented programming.

To match such requirements, any member of the data science team requires both technical and non-technical knowledge and skills — essential for the management of AI-powered models and effective communication of insights for decision-making, as shown below.

DevOps Engineer Roles and Responsibilities

DevOps specialists are responsible for introducing tools, processes, and methodologies across the entire software development lifecycle. They play a key role in combining code, application management, and application maintenance — all to ensure seamless implementation of changes in an application.

Unlike data science specialists, who focus on building and analyzing ML-based models, DevOps-skilled experts specialize in automating deploys, managing infrastructure, and ensuring system reliability. This is directly reflected in the DevOps skill set, which requires expertise in CI/CD pipelines, cloud computing, and security, to name a few. See the extended list of skills below.

MLOps Engineer Roles and Responsibilities

MLOps-skilled engineers stand out by ensuring that machine learning models are not just built but also efficiently rolled out, monitored, and continuously improved in production environments. Unlike data and BI scientists, who focus on model creation, or DevOps experts, who emphasize software reliability, MLOps integrates ML-specific automation, governance, and orchestration to enable high-performing AI-powered systems.

Shortly put, a Machine Learning Operations engineer oversees model production, testing, and code optimization to minimize latency and ensure smooth, hassle-free execution. They also focus on automating workflows, monitoring model performance, optimizing resource utilization, ensuring scalability, and implementing retraining strategies — all of this to maintain accuracy, database efficiency, and infrastructure reliability over time.

MLOps Engineer Salary Insights

The differences in required skill sets for the above-mentioned roles are reflected in their average salaries.

ML Ops Engineer salary is among the highest in the tech industry, and there’s a reason for it. With the high demand for MLOps engineers and a limited talent pool, their role in optimizing infrastructure, reducing costs, and ensuring model accuracy makes them essential for businesses — and hence, continuously driving up salaries.

In the meantime, salary is heavily influenced by a range of factors. Let’s explore them below.

MLOps Salary Range (USA) Based on Level of Experience

MLOps Engineer Salary Range by Hiring Location

While countries like the USA and Switzerland offer the highest salaries for MLOps-oriented engineers, Ukraine stands out as one of the most attractive destinations for hiring top-tier MLOps talent. In particular, what makes Ukraine particularly appealing is the balance between cost and capability. See the image below illustrating this advantage over other hiring locations.

*Ukrainian salaries are provided based on Mobilunity’s Recruitment Team research on the local job markets. All salaries are net and do not include the service fee (in case of hiring on a dedicated team model). The salaries are provided for comparison purposes and could be not entirely accurate. Contact us to know the exact ML Ops salary estimates and the cost of hiring a developer corresponding to the required parameters.

Apart from the factors mentioned above, here are additional aspects that may influence both salary and total project cost:

• Specialized expertise — engineers with advanced tool proficiency and strong skills in security, compliance, and ML governance typically command higher salaries;
• Industry — sectors like finance, healthcare, and autonomous technology offer higher compensation due to their critical reliance on robust ML systems;
• Project complexity — projects involving large amounts of data, multi-cloud setups, or compliance-sensitive systems demand higher compensation;
• Freelance or contract-based experts have higher hourly rates.
  

Must-Know Services Leveraged by MLOPs Experts Your Business Can Benefit From

Machine Learning Model Lifecycle Management Pipeline

Skilled MLOps specialists can help you build, deploy, and manage the entire model development life cycle, ensuring optimal performance and reliability. This ensures faster model’s time-to-launch, reduced downtime, improved model accuracy, lower operational costs, and scalable AI-powered solutions that enable businesses to make data-driven decisions with confidence and efficiency.

ML Model Versioning and Iteration

After implementation in a particular industry, ML models need to be constantly versioned and updated. To do so, MLOps experts leverage automation for model tracking, rollback mechanisms, and experiment management, which reduces potential risks while optimizing model performance and accuracy.

Model Monitoring & Management

Without continuous monitoring, businesses risk operational inefficiencies, compliance failures, and financial losses — ML models degrade over time due to data drift, concept drift, and changing real-world conditions. This can lead to inaccurate predictions and poor business decisions.

Fortunately, here’s where MLOps-skilled engineers step in. They ensure reliable and robust AI-powered systems that drive business value and maintain a competitive edge.

ML Model Governance

Models can be prone to bias, exposing sensitive data, or violating legal frameworks. To ensure fairness, security, and compliance, MLOps specialists skillfully implement various governance policies that prevent unintended consequences and maintain AI integrity. For businesses, this means ethical practices, protected data, and industry compliance — thus impacting trust and operational success.

Model Security Enhancement and Management

When it comes to the threat of data breaches, no business is immune. According to recent studies, the average cost of a data breach in the USA reaches up to $9.36 million — a staggering amount even for large enterprises. However, with the assistance of skilled MLOps experts, businesses can mitigate this risk. Seasoned MLOps specialists are equipped with multiple advanced security practices to provide bulletproof protection against cyber threats.

ML Model Discovery

With this service, businesses can efficiently identify the most effective machine learning models that align with their goals, optimize performance, and maximize ROI. Strategies like automated testing, feature selection, and performance benchmarking help engineers ensure they deploy the most accurate, efficient models for real-life application.

Why Hire Machine Learning Engineers? Cost-Related and Other Benefits

As mentioned earlier, Machine Learning and Artificial Intelligence enable individuals and enterprises to leverage advanced AI applications that save time, optimize workflows, and unlock new revenue opportunities. Such an approach has already yielded results for numerous businesses across various industries.

For example, some MLOps-powered business successes include:

• Slashing operating costs by 50%;
• Boosting ML development and software engineering productivity by 55%;
• Cutting model deployment time from 12 months to just 30-90 days;
  Leveraging the ability to scale the number of experiments by 10 times with the same effort.

Using MLOps Across Industries: The Impact It Makes

Beyond these business results, MLOps-specialized engineers excel at solving industry-specific challenges. Whether you plan to leverage an ML-powered fraud detection solution in finance or hire OCR developers for next-level healthcare data extraction, MLOps play a vital role in delivering and optimizing cutting-edge technologies across various sectors.

To find out more insights about them, explore the most common industries and their key use cases in the banner below.

Determining If MLOps Is The Right Choice for Your Business

While MLOps-oriented services offer substantial benefits, not all businesses require its full capabilities. Its value depends on factors such as the complexity of AI workflows, the scale of the machine learning project, and the need for automation, compliance, and scalability.

If you’re unsure whether to invest in MLOps, here are three key indicators that it’s time to hire an ML-skilled engineer:

> Your company struggles to leverage and manage ML models manually

If deploying, monitoring, or replacing ML models is slow, inconsistent, or riddled with errors due to incompatible IT processes, an MLOps-proficient engineer can revamp these operations for you.

> You’re planning to scale or optimize your ML processes

Whether you’re implementing machine learning for the first time or making tweaks to existing workflows, MLOps-skilled engineers help bridge the gap between model development and real-life application, ensuring efficiency, reproducibility, and scalability.

> You’re a mature company looking to enhance ML model performance

If outdated models hinder business efficiency or fail to integrate smoothly with newer systems, an MLOps expert can modernize and retrain new models, improving accuracy and long-term reliability.

> You’re managing complex multi-cloud or hybrid ML environments

When operating across multiple cloud providers or on-premise environments, dedicated ML engineeers optimize ML-based operations, maintain compliance, and ensure model portability across platforms like AWS, GCP, and Azure.

Selecting the Right-Fit Expert: MLOps Engineer Skills and Top Considerations

Finding a top-tier Senior MLOps-specialized engineer requires more than just assessing technical proficiency. When assessing the candidate, we suggest meticulously evaluating their ability to align with your business objectives and integrate seamlessly into your team. See a sample of a well-crafted resume to help you identify the ideal MLOps-proficient candidate for your project.

If you’re looking for an ideal candidate profile similar to the one above, Mobilunity can help you hire elite MLOps professionals to drive efficiency and scalability in AI-driven operations. Whether you need to boost your ML-based workflows, reduce costs, or enhance model reliability, we provide pre-vetted, highly skilled engineers tailored to your business needs.

Hire MLOps Engineers with Mobilunity

While several outstaffing hubs exist worldwide, Ukraine stands out as a top choice for companies seeking skilled tech professionals. Ukrainian developers are not only highly proficient in cutting-edge technologies but also fluent in English, hardworking, and self-motivated — making them a preferred option for global businesses.

Mobilunity is a leading IT outstaffing provider in Ukraine, helping startups, SMBs, and multinational enterprises source, assess, and hire top developers worldwide. With deep market expertise and a strong talent network, our recruiters accelerate the hiring process, ensuring businesses quickly find the right tech talent to scale their teams.

Need a skilled MLOps developer? Connect with us to find the right MLOps expert!

Written by:

Anastasia Stefanuk

Market Researcher

Disclaimer: All salaries and prices mentioned within the article are approximate numbers based on the research done by our in-house Marketing Research Team. Please use these numbers as a reference for comparison only. Feel free to use the contact form to inquire on the specific cost of the talent according to your vacancy requirements and chosen model of engagement.