MLOps: rynek
MLOps is the engineering of the machine-learning model lifecycle: reproducible training, model registry, production rollout, quality and data-drift monitoring. We track 2 195 open positions listing MLOps, 2 059 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 LLM. 34% of the postings allow fully remote work. Below is measured MLOps demand.
Rynek MLOps ma obecnie 2195 otwartych ról, w tym świeżo zaobserwowanych: 2059. Median salary $10 507/mo. Observed candidate pool — 356.
34% of MLOps postings allow fully remote work; the share counts only postings that state the work format.
⚠ salary known for 176 of 2195 jobs; remote share from 1759 with a stated format; 2059 counted as fresh observations; trend and hiring difficulty are not shown
Popyt i obserwowana podaż
| Otwarty popyt | 2195 |
| Obserwowana podaż | 356 |
⚠ candidates matching a vacancy are not counted yet: demand and the observed pool are shown
Rozkład wynagrodzeń
Co dziesiąta osoba zarabia poniżej $4250, co dziesiąta powyżej $17 916. Połowa rynku mieści się między $6250 a $15 416. Próba: 176.
The range histogram for MLOps postings uses only postings with an explicit compensation range; the median is computed over the same sample.
Geografia popytu
| kraj | oferty |
|---|---|
| US | 441 |
| PL | 306 |
| GB | 177 |
| IN | 143 |
| DE | 132 |
| FR | 61 |
| CA | 57 |
| RU | 45 |
| SG | 42 |
| BR | 40 |
The top country for MLOps demand is the US (441 positions).
⚠ job counts only: salary by country is not published
Używana razem z
MLOps most often appears next to Python, AWS and LLM.
Role, które jej używają
| rola | oferty |
|---|---|
| ML/AI | 788 |
| AI Engineer | 448 |
| Data | 282 |
| DevOps | 219 |
| Architect | 150 |
| Platform Engineer | 57 |
| Fullstack | 46 |
| Backend | 36 |
| Product | 26 |
| Security | 19 |
⚠ the `Other` and `IT` buckets are excluded as non-roles
Popyt według poziomu
| poziom | oferty |
|---|---|
| senior | 754 |
| principal | 204 |
| lead | 163 |
| middle | 129 |
| junior | 35 |
Senior-and-above is about 87% of 1 285 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
Pracodawcy
Popyt rozkłada się na 1189 pracodawców. Największy odpowiada za 2.3%, pierwsza dziesiątka za 10.2%; pozostałe 89.8% to długi ogon.
| udział | wartość |
|---|---|
| top-1 | 2.3% |
| top-3 | 5.0% |
| top-10 | 10.2% |
| długi ogon | 89.8% |
⚠ names are not shown: staffing agencies and end employers are not yet told apart by the classifier
Gdzie Zorky widzi ten rynek
Obserwowane w 100 źródłach; największe odpowiada za 22.2% — ten rynek nie opiera się na jednym kanale.
MLOps postings arrive mostly from ATS & career sites and job boards & aggregators.
Najnowsze ogłoszenia
- Manager, Desjardins Artificial Intelligence Centre of Excellence
- AI Specialist · PL
- Machine Learning Operations (MLOps) Engineer · US
- Senior AI Product Manager · MG
- Machine Learning Engineer · GB
- Founding AI Engineer · GB
- Machine Learning Operations-Engineer II · US
- AWS Solution Architect · AE
Sąsiednie rynki
⚠ adjacent markets for comparison are not defined yet
Jak to mierzymy
- Oferta
- an open job that cleared the quality gate and lists at least two technologies
- Obserwowany kandydat
- a candidate whose stack contains this technology; an aggregate — not a single record leaves the perimeter
- Kandydat do dopasowania
- not counted yet
- Okno
- jobs open at the moment the snapshot was built
O danych
- Część przekrojów jest ukryta: pokrycie danymi jest jeszcze niewystarczające.
- Statystyki pokazujemy tylko tam, gdzie próba przechodzi próg jakości.
- Brak bloku nie oznacza wartości zero.
Obecnie ukryte przekroje: 7.
Dane na dzień 2026-10-26
Najczęściej zadawane pytania
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 2 195 open MLOps positions; demand grows as teams move from experimenting with models to operating them.