Our previous analysis of the Job Market for Machine Learning Engineers proved popular amongst other bloggers! So we have decided to write about MLOps...
As we step into 2025, the demand for MLOps engineers – specialists who bridge the gap between model development and production – is surging. I recently analyzed LinkedIn Talent Insights data on MLOps roles, and the findings paint a compelling picture for both job seekers and hiring managers. In this article, we'll explore salary trends, skills in demand, hiring hotspots, the employer landscape, and career growth opportunities in the MLOps field. The tone here is formal and data-driven, but I’ll also share my personal observations to give some real-world color to the stats and charts.
A Rapidly Growing Talent Pool
MLOps engineering has transformed from a niche specialization into one of the fastest-growing roles in tech. In fact, LinkedIn’s Emerging Jobs report identified MLOps as a standout, with a 9.8× growth in five years. This explosive growth reflects how vital MLOps has become: organizations realized that building machine learning models is one thing, but deploying and maintaining them in production is a whole new challenge.
According to LinkedIn Talent Insights (see Screenshot 1 above), there are now tens of thousands of MLOps professionals in the United States, and that talent pool continues to expand. Many of these professionals made career pivots from software engineering or data science backgrounds. Companies are learning that without robust MLOps practices, even the most brilliant AI models never reach the end user.
The hiring demand is equally robust. LinkedIn data shows thousands of open MLOps positions globally, with a particularly high concentration in the U.S. This demand is fueled by companies across industries racing to productionize AI. Multiple competitive offers are now common for seasoned candidates (sometimes with 48-hour decision windows), and recruiters report that compensation for ML/MLOps roles has jumped ~20% year-over-year. If you have MLOps skills, 2025 is your market.
In-Demand Skills for MLOps Engineers
What exactly are employers looking for in an MLOps engineer? The role sits at the intersection of software engineering, data engineering, and machine learning, so the required skill set is broad. Below is a snapshot of trending skills from LinkedIn’s data:
From this data, we see employers requiring a range of competencies, including:
- Cloud platforms (AWS, Azure, GCP)
- Containerization and orchestration (Docker, Kubernetes)
- Machine Learning frameworks (TensorFlow, PyTorch, Scikit-learn)
- MLOps platforms (Kubeflow, MLflow, CI/CD)
- Programming (Python is typically a must, plus familiarity with Linux, shell scripting, etc.)
- Data engineering basics (ETL, databases)
- DevOps and automation (Jenkins, GitHub Actions, Terraform, CloudFormation)
- Soft skills (communication, cross-functional collaboration)
Because the technology landscape changes fast, many hiring managers say the ability to learn new tools is more critical than mastery of any specific platform. As a candidate, if you can demonstrate an eagerness to adapt, that often outweighs checking every tool off the list.
Salary Trends and Compensation Insights
It’s no secret that MLOps engineers are well compensated – these roles are often as lucrative as other AI positions.
- LinkedIn Data for Software Development: $179,600 in median base salary is one data point cited for U.S. professionals in software-related roles. We see MLOps salaries in a similar or slightly higher range, owing to the specialized skill sets needed.
- External Sources (e.g., Glassdoor, Levels.fyi): Suggest that experienced MLOps engineers commonly earn total compensation packages above $150K. Senior roles at major tech firms can exceed $200K when factoring in bonuses and stock options.
Geography also plays a role in compensation. San Francisco, Seattle, and New York typically offer the highest salaries, reflecting the competition in those areas. However, a rise in remote positions means skilled candidates in lower-cost-of-living regions can sometimes command near-parity pay.
Hiring Hotspots and Talent Geography
Where are MLOps engineers being hired the most? Major tech hubs remain prime markets: the Bay Area (San Francisco/Silicon Valley) tops the list, followed by New York City, Seattle, and Boston. However, we’re seeing emerging hubs (such as Dallas-Fort Worth, Atlanta, and Washington D.C.) gain traction.
- San Francisco Bay Area: Home to leading tech and AI startups.
- Seattle: Dominated by Amazon/AWS and Microsoft.
- New York City: Big finance and advertising tech.
- Austin, Atlanta, D.C.: Rapidly growing ecosystems, attracting talent with cost-of-living advantages.
For both candidates and employers, these insights can shape decisions about where to focus job searches and recruiting efforts. Fintech, Healthcare, and Consulting industries are actively poaching talent from traditional tech hubs, offering remote or hybrid roles.
Employer Landscape: Who’s Hiring MLOps Talent?
The employer landscape for MLOps engineers spans a broad range of company types:
- Tech Giants
- Amazon (AWS), Google, Microsoft, Meta
- Large-scale AI infrastructure, high-paying roles
- Cloud Providers
- AWS, Azure, GCP
- Building tools for MLOps at massive scale
- Finance and Consulting
- JPMorgan Chase, Capital One, Deloitte, Booz Allen Hamilton
- High-salary offers, stable environments, unique data problems
- Healthcare
- CVS Health, UnitedHealth Group, Johnson & Johnson
- Huge data sets, critical real-world impact
For hiring managers, competition isn’t limited to other tech companies; banks, hospitals, retail giants, and smaller AI-driven startups are all vying for MLOps professionals. If you’re at a non-traditional tech firm, be prepared to pitch why your problems (and compensation) are compelling.
Career Growth and Opportunities
The career outlook for MLOps engineers in 2025 is exceptionally bright. Since the discipline is relatively young, you can climb the ladder quickly as you gain experience. Titles like “MLOps Lead,” “ML Platform Architect,” and “Head of MLOps” are becoming more common, reflecting the strategic importance of AI production pipelines.
For candidates, having a unique blend of software engineering, ML, and DevOps skills puts you in high demand. For hiring managers, investing in training and clear career paths for MLOps engineers is crucial for retention. Some organizations even rotate data scientists through MLOps roles, so they better understand production constraints.
Final Thoughts
The job market for MLOps engineers in 2025 can be summed up in one word: thriving. We see a convergence of factors driving this:
- Cross-Industry AI Adoption: Banks, retailers, hospitals, and more want to operationalize their machine learning.
- Evolving Tooling: New MLOps platforms (e.g. Kubeflow, MLflow) require dedicated experts.
- Short Supply of Qualified Candidates: The skill set is broad, and there aren’t enough experienced professionals.
At People In AI, I’ve watched MLOps become a make-or-break role in AI-centric organizations. For candidates, now is the time to level up your DevOps, cloud, and ML pipeline skills—and negotiate confidently. For hiring managers, create an attractive environment that invests in MLOps resources, and be ready to compete on compensation and growth opportunities.
Whichever side of the table you’re on, the possibilities for MLOps in 2025 are immense. Companies will continue to push AI from research to production, and MLOps engineers will be the critical linchpins ensuring that these efforts deliver real-world value.
References & Further Reading
- LinkedIn Talent Insights – MLOps Engineer data (2025).
- People In AI Blog – “Tech Talent on the Move, Senior AI Jobs, MLOps & More...” (Feb 2025).
- People In AI Blog – “Old Money, New Tech: Wall Street and Healthcare…” (Q1 2025).
- DataCamp – “The Best Machine Learning Jobs in 2025 and How to Land Them.”
- KDnuggets – “Top 10 High-Paying AI Skills to Learn in 2025.”
