AI engineer recruitment agency and headhunters
your next AI engineer, in good hands.
We combine our data, our sector experts and our AI algorithms to identify the headhunter most relevant to your role, from the machine learning engineer deploying their first model to the profile industrialising an entire AI platform.
- no setup fees, pay on hire
- guaranteed replacement
they hire their data and AI profiles with muzzo
how do you hire an AI engineer through a recruitment agency?
An AI engineer is judged by the model that runs in production, not the notebook that works once : latency kept, inference costs under control. Muzzo gives your role to the headhunters who hire the most in machine learning. 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 stack. The rest is scoped in your first conversation with the Muzzo team.
02
our data chooses the headhunters
Our AI picks those who have hired the most AI engineers, from more than 2,500 selected headhunters.
03
you meet the best profiles
Approached and qualified by headhunters who specialise in machine learning. You choose who you meet.
04
you hire, then you pay
Not before. If your new hire leaves within 3 months, we restart the search at no cost.
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 role
Antoine and the Muzzo team help you scope your role and choose the right headhunters. Describe your role in the form: we'll call you back to discuss it.
Antoine GigomasCEO and co-founder of Muzzo
one last question?
With Muzzo, you set the fee, between 5 and 30%, and you only pay it on hire: no deposit, no fixed fees. A junior MLOps engineer industrialising a first pipeline and a lead AI engineer piloting several models in production don't take the same search. The fee follows that difficulty, and the Muzzo team helps you set it.
You have a double guarantee. If your new hire leaves within 3 months, for example because the real stack doesn't match the brief, we restart the search at no cost. If we find no replacement within 4 months, we refund 50% of the fees. On average, 94% of Muzzo hires are still in post after 3 months.
machine learning headhunters are ready to start
A hire signed in under 26 days. You only pay when you hire.
the reality
an AI engineer is judged by the model running in production, not the demo
a prototype that works once is not a model in production
Notebook, test set, good accuracy: none of that tells you how the model holds up with real users, with latency and inference cost to watch every day.
LLM, vision or scoring: the same title covers very different stacks
An AI engineer who put an LLM in a product doesn't necessarily master a vision pipeline or an in-house scoring model. The headhunter checks the real stack, not the buzzword.
a hiring mistake shows up at the first model incident
Performance drift undetected, inference cost spiralling, model stuck in prototype phase: the headhunter checks what the profile actually delivered in production, not just what they trained.
the roles
the AI engineering profiles our headhunters go after for you
From the first model in production to the team industrialising an entire AI platform, each search goes to a headhunter who has already hired that profile.
machine learning engineer
They train, evaluate and deploy scoring, recommendation or forecasting models. The most requested role.
MLOps engineer
They industrialise the model lifecycle: training pipelines, deployment, monitoring, automatic retraining.
generative AI engineer
They integrate LLMs into a product: prompting, RAG, light fine-tuning, and monitor inference cost.
computer vision engineer
They train image detection or classification models, often under hardware or real-time constraints.
NLP engineer
They structure and process text: extraction, classification, summarisation, before or alongside generic LLMs.
lead AI engineer
They lead the team, choose the architecture of models in production and become the CTO's or data director's go-to person.
the method
a machine learning headhunter, not a generic IT recruiter
A model running in production is judged by what it holds over time. So the method starts from what the role has to deliver.
we scope the model to put into production
LLM, vision, scoring, recommendation: the brief describes the real stack, available data and what the model must hold when exposed to users.
a headhunter who hires AI engineers
From more than 2,500 selected headhunters, your role goes to those who hire the most in machine learning and MLOps.
a model in production verified, not an isolated notebook
The headhunter asks the profile to walk through a model that actually left prototype stage: its deployment, its monitoring, its most striking incident. Then they cross-check with references.
the figures
an AI engineer's salary in figures
source: Apec, private-sector managerial pay in 111 job families, 2025 edition, p. 62 (Big Data) (opens a new window), 2025 data
source: Apec, private-sector managerial pay in 111 job families, 2025 edition, p. 62 (Big Data) (opens a new window), 2025 data
source: Apec, private-sector managerial pay in 111 job families, 2025 edition, p. 62 (Big Data) (opens a new window), 2025 data
hiring an AI engineer: what you need to know
why use a headhunter to hire an AI engineer?
The AI engineer turns a trained model into a service that holds the load: they choose the deployment architecture, monitor performance drift and control inference cost. Good profiles are in post, held up by deployment work, and don't answer job ads.
A headhunter specialised in machine learning knows how to reach them and ask the right questions: which models actually delivered, which stack, which user volume. This page covers the AI engineer role itself; for all data and AI trades, see our data and AI page.
MLOps or data science: what changes for hiring?
A data scientist explores, tests hypotheses and builds a first model. An AI engineer takes that model, or builds one straight for production: reproducible training pipeline, deployment, monitoring, automatic retraining. The two skills rarely overlap in one person beyond a small team.
Decide in the brief whether the role must mostly explore or mostly industrialise: the search changes headhunter and target profile.
how do you assess an AI engineer at interview?
The CV lists frameworks: PyTorch, TensorFlow, LLMOps tools. The interview must show something else: a model they actually ran in production, with its limits and what went wrong once.
A short case study works well: a model that drifts, a monitoring alert, and ten minutes to explain the diagnosis. The headhunter does this first screen before introducing anyone, and checks with references the models the profile says they deployed.
integrating an LLM or training your own model: what changes for the role?
A generative AI engineer assembles existing building blocks: prompting, RAG, light fine-tuning, and must mostly master inference cost and answer reliability. A computer vision or NLP engineer often trains their own model, under stronger data or hardware constraints.
This choice changes the profile to look for more than the salary: according to Apec, both approaches coexist in the same job family, with pay variation mostly linked to sector and seniority, not the technique used.
where to hire an AI engineer: which sector pays best?
According to Apec, an AI engineer's salary in the Big Data family (covering AI engineering) varies mostly by sector: 55 k€ median in retail, 56 k€ in manufacturing, 58 k€ in telecommunications. An AI engineer joining a software house is not paid like one joining a telecoms operator.
On volume, France Travail's 2026 BMO survey counts 18,330 recruitment projects in the broad category of engineers and managers in research, development and IT and telecoms (57.4% judged difficult), concentrated in Paris, Toulouse and Nantes. It doesn't single out AI engineering, but it shows where employer competition is fiercest.
your questions about hiring an AI engineer
Apec doesn't publish a range specific to AI engineering, but to the Big Data family covering it: salary varies mostly by sector, from 55 k€ median in retail to 58 k€ in telecommunications (2025 edition). Seniority and sector matter more than tenure on this role.
According to France Travail's 2026 BMO survey, 57.4% of the 18,330 recruitment projects in the broad category of IT and telecoms engineers are judged difficult by employers, against 43.8% across all trades. It doesn't single out AI engineering, but the difficulty level is close.
A role tilted towards MLOps industrialises models already designed: pipelines, deployment, monitoring. A role tilted towards generative AI assembles LLMs into a product: prompting, RAG, inference cost control. Describe in the brief what the model must do, not just the technology, and the headhunter will target the right profile.
Through a real case rather than their framework list. Our headhunters ask the profile to walk through a model they actually deployed, an incident or drift they caught, and verify with references that the model is still running.