MLOps: market
MLOps is the engineering of the machine-learning model lifecycle: reproducible training, model registry, production rollout, quality and data-drift monitoring. We track 1 927 open positions listing MLOps, 1 817 of them fresh. LLMs widened the role: classic pipelines now come with LLM application evaluation, GPU model serving and inference cost management. Most of the demand comes from ML/AI, AI Engineer and Data roles. It most often appears next to Python, AWS and Azure. 37% of the postings allow fully remote work. Below is measured MLOps demand.
The MLOps market currently has 1,927 open roles, 1,817 of them freshly observed. Median salary $8,931/mo. Observed candidate pool — 347.
37% of MLOps postings allow fully remote work; the share counts only postings that state the work format.
⚠ salary known for 118 of 1927 jobs; remote share from 1554 with a stated format; 1817 counted as fresh observations; trend and hiring difficulty are not shown
Demand and observed supply
| Open demand | 1,927 |
| Observed supply | 347 |
⚠ candidates matching a vacancy are not counted yet: demand and the observed pool are shown
Salary distribution
One in ten earns under $4,202, one in ten over $18,125. Half the market falls between $5,565 and $15,416. Sample: 118.
The range histogram for MLOps postings uses only postings with an explicit compensation range; the median is computed over the same sample.
Demand geography
| country | jobs |
|---|---|
| US | 387 |
| PL | 294 |
| GB | 143 |
| IN | 125 |
| DE | 95 |
| CA | 52 |
| RU | 47 |
| FR | 43 |
| SG | 38 |
| BR | 36 |
The top country for MLOps demand is the US (387 positions).
⚠ job counts only: salary by country is not published
Used together with
MLOps most often appears next to Python, AWS and Azure.
Roles that use it
| role | jobs |
|---|---|
| ML/AI | 715 |
| AI Engineer | 370 |
| Data | 257 |
| DevOps | 198 |
| Architect | 129 |
| Platform Engineer | 48 |
| Fullstack | 41 |
| Backend | 27 |
| Security | 19 |
| Product | 19 |
⚠ the `Other` and `IT` buckets are excluded as non-roles
Demand by grade
| grade | jobs |
|---|---|
| senior | 661 |
| principal | 180 |
| lead | 128 |
| middle | 112 |
| junior | 35 |
Senior-and-above is about 87% of 1 116 graded MLOps postings. MLOps requires experience in both ML and infrastructure — junior openings are almost absent.
⚠ demand side only: the grade of the observed pool is unknown for most of it
Employers
Demand is spread across 1,042 employers. The largest accounts for 2.7%, the top ten for 11.0%; the remaining 89.0% is long tail.
| share | value |
|---|---|
| top-1 | 2.7% |
| top-3 | 6.1% |
| top-10 | 11.0% |
| long tail | 89.0% |
⚠ names are not shown: staffing agencies and end employers are not yet told apart by the classifier
Where Zorky sees this market
Observed across 95 sources; the largest accounts for 23.7% — this market does not rest on a single channel.
MLOps postings arrive mostly from job boards & aggregators and ATS & career sites.
Recent openings
- Senior AI/ML Engineer (LLM / CV / OCR) · HK
- Senior Data Scientist / Senior Machine Learning Engineer · PL
- AI Architect · PL
- Lead Solutions Architect – MedTech (Strategic Account) · NL
- Principal Solutions Architect · US
- AI Solution Architect · GB
- AI/ML Engineer Sr. · MX
- [8BE] Data Scientist (AI + ML) · AR
Adjacent markets
⚠ adjacent markets for comparison are not defined yet
How this is measured
- Vacancy
- an open job that cleared the quality gate and lists at least two technologies
- Observed candidate
- a candidate whose stack contains this technology; an aggregate — not a single record leaves the perimeter
- Matchable candidate
- not counted yet
- Window
- jobs open at the moment the snapshot was built
About the data
- Some breakdowns are hidden: their data coverage is not yet sufficient.
- Statistics are shown only where the sample clears a quality gate.
- A missing block does not mean a value of zero.
Breakdowns currently hidden: 7.
Data as of 2026-10-20
Frequently asked questions
Common questions about the MLOps market — short answers from the same data slice.
Which tools appear in MLOps postings?
MLflow or Weights & Biases for experiments, orchestrators (Airflow, Kubeflow, Prefect), Kubernetes and Docker, model serving (Triton, vLLM, KServe), cloud ML platforms (SageMaker, Vertex AI, Azure ML), monitoring.
How does MLOps differ from DevOps?
Besides code, data and models are versioned; quality is checked with model metrics, not just tests; production degrades from data drift even without code changes.
How big is MLOps demand?
The slice tracks 1 927 open MLOps positions; demand grows as teams move from experimenting with models to operating them.