Data
MLOps Engineer Recruitment
Specialist MLOps engineer recruiters working across the US, UK, EU and UAE.
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Pierre C. · AI Program Lead
Overview
MLOps engineers keep machine learning models working once they are live. They build the pipelines that train and release models, monitor performance and step in when results start to slip. MLOps is short for machine learning operations, and takes many of its working practices from DevOps.
Day to day, that can mean automating model testing and releases, running models through Kubernetes or cloud services such as SageMaker, Vertex AI or Azure ML, and tracking accuracy, speed and cost. When the data changes, MLOps engineers retrain and release the model again without disrupting the systems around it.
Consortia helps companies hire MLOps engineers on permanent and contract terms across the UK, Europe, the US and the UAE. The same work is also advertised as ML platform engineer, AI platform engineer or AI infrastructure engineer, and as LLMOps where the models are large language models.
We recruit them alongside machine learning engineers and data engineers, so we can help decide which one a team needs.
How we recruit MLOps engineers
We start with how many models the business runs and who looks after them today. A team with one or two models can often cover MLOps within a machine learning engineer role. Once several are live, a dedicated hire usually makes sense. We agree that with the hiring manager before we search.
MLOps engineers tend to come from two directions. Some are DevOps or platform engineers who moved towards machine learning. Others are machine learning engineers who moved towards infrastructure. We ask which side a candidate is stronger on, because few are equally deep in both.
We ask candidates to describe a model they have owned after launch: how they released it, how they monitored it, when they retrained it and what it cost to run. We also check which tools the team uses and how fixed that requirement is, since experience on one cloud usually carries to another.
Few people hold the MLOps title, so most of the search is a direct approach to engineers doing the work under other names. Salary, location and office days are settled on the first call.
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Salary guide
MLOps Engineers salaries
These ranges reflect what employers are advertising right now. Accepted offers can look different. Speak to a consultant about what's actually getting accepted.
We track what UK employers are advertising for this role. Ask us for the current ranges.
US salaries often run ahead of UK ranges, and vary with region and funding stage. Tell us where you're hiring and we'll share what's being advertised.
European salaries vary widely by country. Tell us where you're hiring and we'll share what's being advertised.
UAE packages are often structured differently, with tax-free salaries and allowances. Tell us what you're hiring for and we'll share what's being advertised.
Live jobs
Live Data jobs
- Permanent
Product Analyst
Lisbon · Hybrid
€45,000 - €50,000 per annum
View role - Permanent
Data Scientist
Remote
£80,000 - £110,000 per annum
View role - Contract
Data Architect / Data Modeler Contract
London · On-site
£600 - £700 per day
View role - Permanent
Insight Manager
London · Hybrid
£62,000 - £70,000 per annum
View role - Permanent
Data Analyst
Remote
£30,000 - £35,000 per annum
View role - Permanent
Data Engineer
London · Hybrid
£80,000 - £120,000 per annum
View role
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FAQs
Frequently asked questions
What is the difference between a machine learning engineer and an MLOps engineer?
A machine learning engineer typically builds, trains and deploys models. An MLOps engineer focuses on the systems around them: the pipelines, deployment, monitoring and retraining that keep models working reliably over time. In smaller teams one person often covers both. As the number of models in production grows, MLOps tends to become a separate role.
What does an MLOps engineer do?
An MLOps engineer builds and runs the systems that take a machine learning model from development into live use and keep it working. That covers automated training and release pipelines, monitoring for falling accuracy, retraining and cost control. The role sits between machine learning engineering and DevOps.
When do we need a dedicated MLOps engineer?
Usually when several models are live and keeping them running starts to take machine learning engineers or data scientists away from building. Other signs are slow or manual releases, models nobody is monitoring and rising cloud costs. Smaller teams often cover MLOps within a machine learning engineer role first.
What is the difference between MLOps and DevOps?
DevOps is about releasing and running software reliably. MLOps applies the same approach to machine learning, with extra work that ordinary software doesn't need: versioning data and models, tracking experiments, and watching for a model's results drifting as real-world data changes. Many MLOps engineers started in DevOps.
What skills should an MLOps engineer have?
Strong software engineering in Python, plus the tools used to run software reliably: containers, Kubernetes, infrastructure as code and automated release pipelines. On top of that sits the machine learning side: packaging and serving models, tracking experiments and versions, and monitoring a model's accuracy over time. Experience with one cloud platform, such as AWS, Azure or Google Cloud, is usually expected.
Should we hire an MLOps engineer on contract or permanently?
Contract suits a defined piece of work, such as building the first release pipeline, moving to a new platform or clearing a backlog of models waiting to go live. A permanent hire suits ongoing ownership, where someone needs to know the models and the business well enough to keep improving how they run. Some teams start with a contractor to set things up, then hire permanently.
What is LLMOps?
LLMOps is MLOps for large language models. The models usually come from a provider or an open-source project, so the work is less about training and more about running them well: managing prompts, testing the quality of output, and keeping cost and response times under control. Many MLOps engineers now cover both.
How do I start working with Consortia?
Clients can get in touch to discuss a brief, and candidates are welcome to register their interest. The quickest route is our contact page or a call to +44 203 397 4565, and one of our consultants will pick things up from there.
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