Specialism 04 · MLOps & AI Infrastructure
MLOps & AI infrastructure for iGaming.
We place the engineers who get models into production and keep them there: deployment, monitoring, scaling and cost. Whether you're hiring for MLOps engineer jobs in iGaming or planning your next move, this is the layer that makes AI real.
MLOps in iGaming
Where models go to actually work.
A model that wins on a laptop is worth nothing until it runs reliably in production. MLOps and AI infrastructure is the discipline that gets it there and keeps it there: packaging and deploying models, serving them at low latency, monitoring for drift and failure, controlling cost, and making it all repeatable. In iGaming, where models touch live, real-money traffic and have to satisfy strict governance, this layer is the difference between AI that ships and AI that stalls.
The engineers who do it well are rare. They sit between data science and platform engineering, fluent in both, and they care as much about reliability, observability and cost as they do about the model itself. As more operators and suppliers move from AI experiments to AI in production, and as LLMs add a whole new operational surface, demand for genuine MLOps experience has run well ahead of supply.
This is squarely our patch. We focus only on AI and data roles in iGaming, so we understand both the engineering and the regulated context it runs in. Whether you're standing up an ML platform, putting your first models into production, or wrestling inference cost under control, we connect you with engineers who have done it before. And if you're an MLOps or platform engineer planning your next step, we'll only show you the roles worth your time.
Who we place
The MLOps and platform roles we recruit.
From hands-on deployment to platform leadership, across operators, studios and suppliers.
The stack
The tools and stack we recruit for.
We recruit MLOps and AI infrastructure engineers across the modern stack, matching real experience to your environment rather than keyword-matching a CV. Common ground includes:
Why a specialist
We know shipped from "works on my machine".
We screen for production reality
We can tell who has run models reliably at scale from who has only deployed a demo. Your shortlist reflects it.
We know the iGaming context
Live traffic, latency, cost and regulated governance. We brief candidates on what makes operating AI here demanding.
A live AI and data network
Active and passive MLOps and platform engineers across Malta, Gibraltar, Cyprus, the UK and remote Europe.
Signal over volume
A tight, relevant shortlist, not a pile of CVs. You spend your time interviewing, not filtering.
FAQ
MLOps hiring, answered.
What does an MLOps engineer do in iGaming?
They get models into production and keep them healthy, deployment, serving, scaling, monitoring for drift and failure, and controlling inference cost, against live, regulated traffic.
What is the difference between MLOps and a machine learning engineer?
ML engineers focus on building models; MLOps engineers focus on deploying, operating and scaling them reliably. The roles overlap and often work side by side.
What salaries do MLOps engineers command in iGaming?
It varies by seniority, location and company, but MLOps is among the better-paid AI disciplines given how scarce real production experience is. We can share current benchmarks.
Do you recruit remote MLOps engineers?
Yes. We place permanent and contract MLOps and platform engineers across Malta, Gibraltar, Cyprus and the UK, and on fully remote roles open across Europe.
Getting AI into production?
Tell us what you're deploying, and we'll find the engineers to run it reliably.