MLOps: market
An MLOps Engineer builds the infrastructure of the model lifecycle: training pipelines, model registry, rollout and serving, quality and data-drift monitoring. We currently track 375 open positions for this role, 351 of them fresh. The core is Python, MLflow or alternatives, orchestrators (Airflow, Kubeflow), Kubernetes and Docker, cloud ML platforms, GPU model serving (Triton, vLLM) and monitoring. By co-occurrence in requirements, MLOps, Python, AWS and Kubernetes appear most often. 35% of the postings allow fully remote work. Below is a live slice of the MLOps Engineer market: geography, seniority, adjacent stack and sources.
The MLOps market currently has 375 open roles, 351 of them freshly observed. Median salary $8,405/mo.
35% of MLOps Engineer postings allow fully remote work; the share counts only postings that state the format.
⚠ salary known for 29 of 375 jobs; remote share from 326 with a stated format; 351 counted as fresh observations; trend and hiring difficulty are not shown
Timeline: postings and sources
How many MLOps postings appeared each week since 4 May, and from how many sources. The second line separates market growth from coverage growth: a jump in the week new sources were connected means we started seeing more, not that the market grew.
New postings and active sources per week
Sources over the period: 106
New sources connected: 11 May +5 · 18 May +3 · 25 May +2 · 1 Jun +5 · 8 Jun +3 · 22 Jun +5 · 29 Jun +12 · 6 Jul +8 · 13 Jul +14 · 20 Jul +5 · 27 Jul +3 · 3 Aug +2 · 10 Aug +4 · 17 Aug +6 · 24 Aug +1 · 31 Aug +4 · 7 Sept +2 · 14 Sept +3 · 21 Sept +4 · 28 Sept +1 · 5 Oct +2 · 12 Oct +5
Week = posting first-seen date, all statuses; postings are selected by the same rule as this page's counter. Sources are distinct collection channels; their names are not published.
Remote work over time
Share of MLOps postings offering fully remote work, per week.
Remote postings, %
The share is among postings that state a format (remote, hybrid, office). Weeks with fewer than 10 such postings are left blank.
Technologies over time
How the share of MLOps postings mentioning each of the top technologies changed.
Postings mentioning the technology, %
Share among postings with a tagged stack; the series starts on 10 Aug: earlier stack tags were assigned differently. Weeks with fewer than 20 tagged postings are left blank.
Countries over time
How the share of MLOps postings in each of the top countries changed, per week.
Postings in the country, %
Share among postings with a known country; the chart shows the five countries with the most postings over six months. Weeks with fewer than 20 postings with a known country are left blank. A rising share can mean a newly connected source in that country rather than market growth.
Demand and observed supply: MLOps
| Open demand | 375 |
| Observed supply | — not published |
⚠ candidates matching a vacancy are not counted yet: demand and the observed pool are shown
Salary distribution: MLOps
One in ten earns under $2,081, one in ten over $15,000. Half the market falls between $4,250 and $10,778. Sample: 29.
The range histogram for MLOps Engineer uses only postings with an explicit compensation range; the median is computed over the same sample.
Demand geography: MLOps
| country | jobs |
|---|---|
| PL | 64 |
| US | 50 |
| GB | 47 |
| IN | 28 |
| DE | 19 |
| FR | 16 |
| CA | 10 |
| RU | 8 |
| NL | 8 |
| ZA | 5 |
The top country for MLOps Engineer demand is Poland (64 positions).
⚠ job counts only: salary by country is not published
Used together with
In MLOps Engineer requirements the most frequently named skills and technologies are MLOps, Python, AWS and Kubernetes.
Demand by grade: MLOps
| grade | jobs |
|---|---|
| senior | 139 |
| principal | 36 |
| middle | 32 |
| lead | 15 |
| junior | 9 |
Senior-and-above is about 82% of 231 MLOps Engineer postings with an explicit grade.
⚠ demand side only: the grade of the observed pool is unknown for most of it
Employers: MLOps
Demand is spread across 228 employers. The largest accounts for 7.6%, the top ten for 22.1%; the remaining 77.9% is long tail.
| share | value |
|---|---|
| top-1 | 7.6% |
| top-3 | 12.5% |
| top-10 | 22.1% |
| long tail | 77.9% |
MLOps Engineer openings come from 228 distinct employers; the largest one accounts for only 7.6% of postings — the market is not tied to a single hirer.
⚠ names are not shown: staffing agencies and end employers are not yet told apart by the classifier
Where Zorky sees this market
Observed across 49 sources; the largest accounts for 12.5% — this market does not rest on a single channel.
MLOps Engineer postings arrive mostly from ATS & career sites and job boards & aggregators.
Recent openings: MLOps
- Développeur IA / MLOps - confirmé (IT) · FR
- Senior MLOps Engineer · GB
- MLOps Engineer (C2H) · US · $15,000
- MLOps Engineer · GB
- Senior MLOps Engineer · GB · $9,851
- Computational Biology MLOps Engineer · US
- FR - MLOps Engineer - Data Infrastructure · FR
- AI & MLOps Engineer (m/w/d) · DE
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: 8.
Data as of 2026-10-24
Frequently asked questions
Common questions about the MLOps Engineer role — short answers from the same data slice.
What does an MLOps Engineer do?
An MLOps Engineer builds the infrastructure of the model lifecycle: training pipelines, model registry, rollout and serving, quality and data-drift monitoring.
Which skills do MLOps Engineer postings require?
The core is Python, MLflow or alternatives, orchestrators (Airflow, Kubeflow), Kubernetes and Docker, cloud ML platforms, GPU model serving (Triton, vLLM) and monitoring. In the current slice, MLOps, Python, AWS and Kubernetes appear in requirements most often.
How does an MLOps engineer differ from a DevOps engineer?
Besides code, they version data and models, check quality with model metrics and watch for data drift, which breaks production without a single code change.
How remote-friendly are MLOps Engineer jobs?
35% of postings with an explicit format allow fully remote work; the slice tracks 375 open positions.
Get the full MLOps market details
Full role analytics — salary bands, employers, trends and available candidates. Leave a contact and we'll send the details.