MLOps : marché
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.
Le marché MLOps compte actuellement 2 195 postes ouverts, dont 2 059 observés récemment. 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
Demande et offre observée
| Demande ouverte | 2 195 |
| Offre observée | 356 |
⚠ candidates matching a vacancy are not counted yet: demand and the observed pool are shown
Distribution des salaires
Un sur dix gagne moins de $4 250, un sur dix plus de $17 916. La moitié du marché se situe entre $6 250 et $15 416. Échantillon: 176.
The range histogram for MLOps postings uses only postings with an explicit compensation range; the median is computed over the same sample.
Géographie de la demande
| pays | offres |
|---|---|
| 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
Utilisée avec
MLOps most often appears next to Python, AWS and LLM.
Métiers qui l'utilisent
| métier | offres |
|---|---|
| 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
Demande par niveau
| niveau | offres |
|---|---|
| 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
Employeurs
La demande est répartie entre 1 189 employeurs. Le plus grand en représente 2.3 %, les dix premiers 10.2 % ; les 89.8 % restants forment la longue traîne.
| part | valeur |
|---|---|
| top 1 | 2.3% |
| top 3 | 5.0% |
| top 10 | 10.2% |
| longue traîne | 89.8% |
⚠ names are not shown: staffing agencies and end employers are not yet told apart by the classifier
Où Zorky voit ce marché
Observé sur 100 sources ; la plus grande en représente 22.2 % : ce marché ne repose pas sur un seul canal.
MLOps postings arrive mostly from ATS & career sites and job boards & aggregators.
Postes récents
- 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
Marchés voisins
⚠ adjacent markets for comparison are not defined yet
Comment c'est mesuré
- Offre
- an open job that cleared the quality gate and lists at least two technologies
- Candidat observé
- a candidate whose stack contains this technology; an aggregate — not a single record leaves the perimeter
- Candidat correspondant
- not counted yet
- Fenêtre
- jobs open at the moment the snapshot was built
À propos des données
- Certaines ventilations sont masquées : leur couverture de données n'est pas encore suffisante.
- Les statistiques ne sont affichées que là où l'échantillon passe un contrôle qualité.
- Un bloc absent ne signifie pas une valeur nulle.
Ventilations masquées actuellement : 7.
Données au 2026-10-26
Questions fréquentes
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.