Analysis of 440 AI/ML Ops Job Posts

Key Findings

• Most roles are mid-level: 82% of AI/ML Ops positions target professionals with 5+ years of experience
• Median salary is $175,000: The middle 80% of roles pay between $111K and $263K annually
• Certifications appear in 11% of posts: AWS Machine Learning Specialty, Google Cloud ML Engineer and Databricks certifications lead requests
• Technology firms dominate hiring: Technology (49%), IT Services (21%) and Financial Services (10%) lead postings
• California leads the market: 26% of U.S. roles are posted in California, followed by New York (8%) and Illinois (7%)
• Degrees not necessary: Only 39% of junior roles require degrees, dropping to 30% at senior levels

The Role of an AI/ML Ops Professional

These patterns align with what we see across AI recruitment, where organizations balance production infrastructure with hands-on technical execution.

We categorized each role by seniority and found the market heavily favors mid-level professionals – they account for more than four-fifths of all postings.

We then extracted experience requirements (78% of roles mentioned a specific number) and calculated the average minimum at each level of seniority. Finally, we analyzed job titles to identify the most common naming conventions at each level.

• Junior (9% of roles)
  • Minimum experience: 3 years
  • Common titles: MLOps Engineer, AI Operations Specialist, Data Science & ML Ops Engineer
• Mid-Level (82% of roles)
  • Minimum experience: 5 years
  • Common titles: MLOps Engineer, Solutions Architect (MLOps), AI/ML Operations Manager
• Senior (9% of roles)
  • Minimum experience: 6 years
  • Common titles: Sr. MLOps Engineer, Manufacturing Operations AI Factory Leader, Director AI Operations & Services

Most AI/ML Ops jobs are mid-level

What Do AI/ML Ops Jobs Involve?

So what is an AI/ML Ops professional actually responsible for day-to-day? We analyzed the language across all 440 job posts to extract the core responsibilities at each level. What emerged is a clear progression of expectations from implementation to strategic vision:

Junior-Level Roles:

• Build deployment pipelines and cloud infrastructure for model training and monitoring
• Manage data quality workflows including collection and auditing
• Support AI initiatives through operational coordination and automation implementation

Mid-Level Roles:

• Architect MLOps platforms accelerating model development-to-production timelines
• Design high-performance GPU infrastructure supporting distributed training and inference
• Enable organizational AI adoption by establishing CI/CD workflows

Senior-Level Roles:

• Define strategic vision and roadmap for ML infrastructure platforms
• Drive digital transformation by embedding AI capabilities across operations
• Oversee portfolio management of AI/ML solutions including resource allocation

Key takeaway: Junior professionals build infrastructure, mid-level leaders architect platforms, senior executives define strategic vision. Each step up means more organizational influence over how ML operates at scale.

Who’s Hiring for AI/ML Ops?

Technology companies lead with 49% of AI/ML Ops postings – nearly half the market. This concentration makes sense given the discipline’s infrastructure focus and the rapid ML adoption within tech firms building both internal systems and customer-facing products.

IT Services follows at 21%, with Financial Services rounding out the top three at 10%. Manufacturing and Healthcare each capture 6% and 4% respectively, completing the top five.

Technology companies account for half of all AI/ML Ops openings

Large companies with 10,001+ employees account for 24% of postings – slightly below the economy’s wider workforce distribution. Organizations with 1,001-10,000 employees add another 22%, meaning roughly half of AI/ML Ops roles are at companies with over 1,000 employees.

What’s striking is the startup concentration – 27% of roles come from companies with fewer than 51 employees. This bimodal distribution reflects both massive platform investments from tech giants and the proliferation of AI-native startups building specialized ML infrastructure.

Startups and enterprises dominate AI/ML Ops hiring

Where Are AI/ML Ops Jobs Located?

One quarter of AI/ML Ops jobs are in California

California dominates with 26% of all AI/ML Ops postings. New York follows at 8%, Illinois at 7%, Texas at 7%, and Massachusetts rounds out the top five at 5%.

The concentration in California reflects the state’s tech hub status, though the geographic spread provides options across multiple markets. Remote roles account for just 11% of postings despite the technical nature of the work, suggesting organizations prefer AI/ML Ops professionals on-site where they can collaborate directly with data science and engineering teams.

Other states worth noting include Washington, Florida, Virginia and Georgia – each capturing roughly 2-3% of the market. What’s notable is the market concentration: nearly half of all states (22) show no AI/ML Ops postings, indicating this remains a specialized function tied to established tech ecosystems.

22 states do not have any job posts for AI/ML Ops

Key takeaway: California offers more than three times as many opportunities as any other state. New York, Illinois and Texas provide the next tier, but expect significantly fewer options – and limited remote work – outside these major tech hubs.

Requirements for AI/ML Ops Jobs

We analyzed the minimum requirements of each job post and found that most AI/ML Ops jobs (55%) do not require a degree. What’s unusual is the pattern at senior levels – degree requirements actually drop significantly.

For junior roles, 61% don’t specify degree requirements, while 34% require a bachelor’s and 5% require a master’s.

Mid-level positions tighten up: 45% don’t specify requirements, 47% require a bachelor’s, and 8% require a master’s.

Senior roles reverse the pattern: 70% don’t specify degree requirements, while just 24% require a bachelor’s and 6% require a master’s.

Degree fields of study that are typically requested of AI/ML Ops professionals include:

• Computer Science (49%)
• Engineering (17%)
• Data Science (9%)
• Applied Mathematics (7%)
• Statistics (6%)
• Mathematics (5%)
• Operations Research (4%)
• Software Engineering (4%)
• Information Systems (4%)
• Business Analytics (2%)

Degrees are not the determining factor for senior AI/ML ops positions

Requested Qualifications in AI/ML Ops Job Posts

AI/ML Ops professionals must excel at containerization, communication and model deployment. Containerization appeared in 36% of listings, communication in 28%, and model deployment in 27% – reflecting the role’s dual nature as both infrastructure engineer and cross-functional coordinator.

Collaboration (17%), data engineering (16%), and cross-functional collaboration (16%) round out the core capabilities, emphasizing the need to bridge data science, engineering, and business teams simultaneously.

Deep technical expertise matters equally. Python programming, cloud platforms, and model monitoring are table stakes, supported by hands-on experience with frameworks like MLOps, CI/CD and Infrastructure as Code.

Just 11% of postings request specific certifications, but when they do, these credentials lead:

• AWS Certified Machine Learning Specialty
• Google Cloud Professional Machine Learning Engineer
• Google Cloud Professional Cloud Architect
• Databricks Certification
• Certified Kubernetes Administrator (CKA)
• Certified Kubernetes Security Specialist (CKS)
• Project Management Professional (PMP)

Key takeaway: Technical depth in containerization and cloud platforms is table stakes, but transformation & change management skills like communication and cross-functional collaboration separate strong candidates from purely technical ones. The shift toward production ML means model governance and observability are becoming core competencies.

What do AI/ML Ops Jobs Pay?

Just under half (47%) of the AI/ML Ops roles we analyzed included an advertised salary.

There was significant breadth in the ranges employers posted, so we normalized the data by selecting the midpoint for our analysis. From our experience, this is generally a much more indicative number for an employer’s target offer – especially in the current market where initial ranges often run wide.

Across the entire dataset of salaries, we found the median salary for AI/ML Ops positions to be $175,000. The middle 80% of salaries (10th to 90th percentile) ranged from $111,365 to $262,500.

AI/ML Ops salaries can vary significantly

Breaking AI/ML Ops salaries down by seniority reveals dramatic progression between junior and mid-level, with more modest increases at senior tiers. Junior roles start at a median $83,492, with mid-level positions jumping 112% to $176,900. The leap to senior adds another 15% – reaching $203,500 median.

The steep junior-to-mid increase reflects the specialized expertise required to architect production ML systems. Senior roles show the tightest clustering – the 75th and 90th percentiles both sit near $220K, suggesting more standardized compensation at leadership levels where strategic scope matters more than technical depth alone.

What’s notable is the overlap between tiers: mid-level roles at the 90th percentile ($270,250) significantly exceed senior roles at the median ($203,500). This compression reflects the premium placed on specialized expertise in high-demand areas like GPU infrastructure or real-time inference systems.

Senior AI/ML Ops professionals are in the top 6% of U.S. earners

Key takeaway: AI/ML Ops positions pay exceptionally well. The median senior-level salary of $203,500 puts these roles in the top 6% of all earners in the United States. Even mid-level professionals earning the median $176,900 land in the top 8%.

Final Thoughts

For Candidates: Build hands-on experience with Docker, Kubernetes and cloud platforms early – these capabilities appear across all levels. For mid-level roles, demonstrating you’ve architected MLOps platforms that accelerated deployment timelines separates candidates. At senior levels, experience defining infrastructure strategy and leading digital transformation matters more than technical execution alone. AWS, GCP or Databricks certifications accelerate credibility.

For Employers: The tight salary clustering around $176,900 for mid-level roles reflects market maturity – fall significantly below that and expect longer time-to-fill. The strongest signal for senior candidates is experience building organizational ML capabilities and establishing governance frameworks, not just maintaining deployment pipelines. Remote flexibility remains surprisingly limited in this field, suggesting MLOps requires substantial on-site collaboration despite the technical nature of the work.

Methodology

We analyzed 440 AI/ML Ops job postings collected from LinkedIn, Indeed and Glassdoor between November 2024 and January 2025. The dataset was limited to full-time roles posted in the United States that explicitly mentioned “MLOps,” “AI operations,” “ML operations” or close variations in the job title.

Duplicate postings were removed using job title, company name and location matching. Seniority levels were determined by analyzing job titles alongside minimum experience requirements stated in each posting. When experience ranges were provided, the lower bound was used for consistency.

Salary data was extracted from the 47% of postings that included compensation ranges. We used the midpoint of each range for analysis, as this most closely reflects employer target offers in practice.

Industry classifications were assigned based on company descriptions and verified against LinkedIn company data where available. Geographic analysis was conducted at the state level using the primary job location listed in each posting.