MLOps engineer recruitment agency and headhunters
your next MLOps engineer, in good hands.
We combine our data, our tech experts and our AI algorithms to identify the headhunter most relevant to your role, from your first automated training pipeline to the AI platform that scales.
- no setup fees, pay on hire
- guaranteed replacement
they hire their data and AI profiles with muzzo
how do you hire an MLOps engineer through a recruitment agency?
An MLOps engineer industrialises AI models already designed: training pipelines, deployment, monitoring, GPU infrastructure. Muzzo gives your role to the headhunters who place this profile, at the crossroads of data, AI and devops. Fee of 5 to 30%, paid on hire. Leaves within 3 months: we restart the search free.
how it works
you only pay if you hire
01
you describe the role
Using the form above: the role, your company, your AI infrastructure. The rest is framed in your first chat with the Muzzo team.
02
our data chooses the headhunters
AI picks those who have placed the most MLOps engineers, among more than 2 500 selected headhunters.
03
you meet the best profiles
Approached and qualified by expert headhunters in data and AI. You choose who you see.
04
you hire, then you pay
Not before. If the hire leaves their role within 3 months, we restart the search free.
they hired with muzzo
Muzzo's strength is the diversity of its recruiters: there is always someone listening to our needs.
With Muzzo, I get the reactivity of a marketplace and the quality of a search firm.
let's talk about your hire
Antoine and the Muzzo team help you frame your role and pick the right headhunters. Describe your role in the form: we'll call you back to discuss.
Antoine GigomasCEO and co-founder of Muzzo
one more question?
With Muzzo, you set the fee from 5 to 30%, and you only pay it on hire: no upfront costs, no setup fees. A junior MLOps engineer operating existing pipelines and a lead MLOps engineer designing the whole platform don't ask for the same search. The fee follows this rarity.
You have double protection. If the hire leaves within 3 months (for example, because the real infrastructure doesn't match the brief), we restart the search free. With no replacement found within 4 months, we refund 50% of your fee. On average, 94% of Muzzo hires are still in place after 3 months.
MLOps headhunters are ready to start
A hire closed in under 26 days, paid only on placement.
the reality
an MLOps engineer is judged by the model that runs every day, not the notebook that worked once
without an MLOps engineer, a model stays a prototype
A data scientist or AI engineer designs a performing model in test. Without reproducible training pipelines or monitoring, that model won't survive its first drift in production.
MLOps, data engineering or devops: these roles are often confused
The MLOps engineer industrialises the model lifecycle. The data engineer builds upstream data pipelines. The devops engineer automates application deployment, rarely of GPU-trained models. The headhunter starts from what your team really asks this role to do.
a hiring mistake shows up in the infrastructure bill
A badly designed pipeline or missing monitoring translates to training and inference costs that spiral. The headhunter verifies a pipeline actually in production before introducing you to someone.
the roles
the MLOps engineer profiles our headhunters will find for you
From your first automated training pipeline to a platform serving multiple AI teams, each search goes to a headhunter who knows this role.
MLOps engineer
They automate training, deployment and monitoring of models, and manage the GPU infrastructure that runs them. The most sought role in this speciality.
lead MLOps engineer
They lead an MLOps team and choose the platform architecture that serves models across the organisation.
ML platform engineer
They build internal tools and self-service platforms that data scientists use to train and deploy their own models.
junior MLOps engineer
They operate existing pipelines under supervision and start automating repetitive tasks in the model lifecycle.
the method
a headhunter of MLOps, not a data or devops generalist
An MLOps engineer who industrialises models and a devops engineer who deploys applications don't recruit from the same networks. The method starts from this distinction.
we frame the platform and real infrastructure
GPU or CPU, cloud or on-premise, tools in place (MLflow, Kubeflow or others): the brief describes real infrastructure, not just 'an MLOps role'.
a headhunter who places MLOps engineers
Among more than 2 500 selected headhunters, your role goes to those who place the most on this specific speciality.
a model pipeline verified, not a tooling list
The headhunter asks what's been deployed to production, the volume of models served and how a drift has already been detected and fixed.
the figures
the MLOps engineer in figures
source: Stack Overflow Developer Survey 2025, Work section, 'Salary by developer type' (opens a new window), 2025 data
source: Stack Overflow Developer Survey 2025, Work section, 'Salary by developer type' (opens a new window), 2025 data
source: Stack Overflow Developer Survey 2025, Developers section, 'Role' (opens a new window), 2025 data
source: Stack Overflow Developer Survey 2025, Developers section, 'Role' (opens a new window), 2025 data
hiring an MLOps engineer: what you need to know
why hire an MLOps engineer instead of another data scientist?
An MLOps engineer doesn't design new models: they take an already-trained model and build everything that lets it run reliably, at scale and over time. Without this work, a team accumulates notebooks that work in test but they don't know how to run in production.
That's why many organisations hire an MLOps engineer when they move from the first experimental model to several models running continuously. For model design itself, see our AI engineer page.
MLOps engineer or devops engineer: what difference?
The devops engineer automates deployment of classic applications: APIs, web services, databases. The MLOps engineer applies similar logic, but to trained models: data and model versioning, GPU infrastructure, performance drift detection.
A generalist devops engineer rarely industrialises a training pipeline without support. Specify in the brief whether the role should mostly serve AI models or classic applications: for the latter, see our devops engineer page.
how do you evaluate an MLOps engineer in interview?
The CV lists tools: MLflow, Kubeflow, Airflow, cloud platforms. The interview must show something else: a training pipeline they've actually automated, and a model drift they've detected in production before it became a problem.
A practical case works well: a model whose performance is degrading, a monitoring alert, and ten minutes to explain the diagnosis and the fix. The headhunter does this first filter before introducing you to someone, and verifies in reference calls the pipelines claimed.
MLOps engineer: a role still rare, but already well-paid
No public survey yet names MLOps engineer as a distinct category: it's a new role, at the crossroads of two more established specialities. In the Stack Overflow Developer Survey 2025, which polled more than 43 500 developers worldwide, the two closest categories, 'AI/ML engineer' and 'DevOps engineer or professional', represent 1.39% and 2.32% of respondents respectively.
Both categories also show close, high global median salaries: $89,427 for the AI/ML engineer, $87,011 for the DevOps engineer or professional. The MLOps engineer, combining both skill sets, typically recruits in this high range, with a premium tied to rarity rather than seniority.
your questions on MLOps engineer recruitment
The AI engineer designs and trains models. The MLOps engineer takes these models and builds everything that makes them run reliably: pipelines, deployment, monitoring, GPU infrastructure. In a small team, one profile often does both; beyond that, the roles separate.
The devops engineer automates application deployment. The MLOps engineer applies similar logic, but to trained models, with GPU infrastructure and model-specific data versioning. A generalist devops engineer doesn't always know how to industrialise a model pipeline without support.
According to the Stack Overflow Developer Survey 2025, the two closest categories to MLOps engineer, 'AI/ML engineer' and 'DevOps engineer or professional', remain minority roles among developers (1.39% and 2.32% of 43 560 respondents respectively), but show global median salaries among the highest in the survey ($89,427 and $87,011), a sign of rarity already valued.
The headhunter asks for a concrete case: a training pipeline actually automated, a model drift detected and fixed, and the infrastructure used. A list of tools on a CV isn't enough without this track record.