Data
MLOps Engineer Recruitment
Specialist MLOps engineer recruiters working across the US, UK, EU and UAE.
Trusted by tech teams from start-ups to globally recognised brands
“Extremely responsive and professional service. Had a great recruitment experience. Highly recommend Consortia as a talent acquisition partner.”
Pierre C. · AI Program Lead
Overview
How Consortia recruits MLOps engineers
Consortia provides MLOps engineer recruitment and staffing for companies across the UK, Europe, the US and the UAE, on permanent and contract terms. We recruit across Machine Learning, Data and Engineering, which helps when a brief sits somewhere between an MLOps Engineer, Machine Learning Engineer, DevOps Engineer or Platform Engineer.
We start with what is already running in production and who looks after it day to day. A team with one or two machine learning models may be able to cover MLOps within a machine learning engineer role. As the number of models grows, or the infrastructure becomes more complex, companies are more likely to need a dedicated MLOps Engineer or ML Platform Engineer.
The title itself varies. We also search for ML Platform Engineers, AI Platform Engineers and AI Infrastructure Engineers, as well as LLMOps specialists where the infrastructure is supporting large language models. That matters because relatively few candidates use “MLOps Engineer” as their exact job title.
People tend to arrive in MLOps from two directions. Some are DevOps or platform engineers who have moved closer to machine learning infrastructure. Others are machine learning engineers who have become more focused on deployment, reliability and production systems. We establish which side of the role matters most before we search, because few candidates are equally deep in both.
When we speak to candidates, we ask them to take us through a model they have supported after launch. We want to understand how it was deployed, how performance was monitored, what happened when it needed retraining and how they managed reliability and cost once it was live.
We also look at the infrastructure and tooling around the role. That can include Kubernetes, SageMaker, Vertex AI, Azure Machine Learning and cloud platforms such as AWS, Google Cloud and Azure. We agree with the hiring manager which technologies are essential from day one and where experience can transfer from one platform to another.
Few strong MLOps candidates are found through job adverts alone, particularly when the search includes people working under adjacent titles. We approach engineers directly across our Machine Learning, Data, DevOps and Platform networks, review every profile ourselves and confirm salary, location and office expectations before an introduction.
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Short-term and fixed-term specialists for projects and interim cover.
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Retained search for technology leaders and hard-to-find specialists.
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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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Data engineers across pipelines, platforms and ML infrastructure, permanent and contract.
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Machine learning engineers who train, deploy and maintain models in production, permanent and contract.
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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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