Specialists who build and maintain the infrastructure that powers machine learning models. A critical role in scaling AI from research to production.
MLOps Engineers are the backbone of modern AI development, ensuring that machine learning models can be deployed, monitored, and scaled efficiently. They work at the intersection of data science, software engineering, and DevOps, focusing on the entire lifecycle of AI models. From setting up data pipelines to automating model deployment, MLOps Engineers play a crucial role in bridging the gap between research and production.
A typical week is split between developing and maintaining infrastructure, monitoring model performance, and collaborating with data scientists and engineers.
Based on advertised midpoints across the 9 UK listings priced in pounds in the last 12 months. Base salary only.
% of 9 listings posted in the last 12 months that mention each skill, extracted from job descriptions.
A typical UK progression for mlops engineers. Years are guidance — strong people move faster, and many senior folks sidestep into research, product or management.
Assist in setting up and maintaining data pipelines and CI/CD workflows. Work under supervision to support model deployment and monitoring.
Own the development and maintenance of MLOps infrastructure. Lead the integration of models into production systems and ensure smooth deployment processes.
Oversee the entire MLOps lifecycle, from data ingestion to model monitoring. Mentor junior engineers and drive best practices in MLOps.
Strategise and lead the MLOps function, driving innovation and efficiency. Influence organisational MLOps standards and mentor senior engineers.
There's no single route, but most people follow some version of these steps.
Start by gaining a solid understanding of data engineering, DevOps, and machine learning fundamentals. Familiarise yourself with tools like Docker, Kubernetes, and TensorFlow.
Work on personal or open-source projects to gain hands-on experience with MLOps. Develop and deploy machine learning models to production environments.
Join a tech company or startup to work on real-world MLOps challenges. Collaborate with data scientists and engineers to streamline model deployment and monitoring.
Focus on advanced MLOps topics such as automated model retraining, model versioning, and scalable infrastructure. Contribute to the MLOps community through blogs and talks.
Take on leadership roles, managing MLOps teams and driving strategic initiatives. Develop and implement best practices for MLOps in your organisation.
Become a thought leader in MLOps, influencing industry standards and best practices. Mentor the next generation of MLOps professionals and contribute to the broader AI community.
Adria Solutions Huddersfield £35,000 – £45,000 pa
Supports AWS infrastructure, CI/CD pipelines and automation for software and machine learning workloads, including packaging, versioning and deploying ML models. Works alongside software and AI engineers to keep development, testing and production environments consistent, monitoring pipelines and fixing issues. Suited to an early-career engineer with cloud, scripting and Git foundations and an interest in MLOps.
Aioi Nissay Dowa Europe Oxford £78,174 – £94,686 pa
Contract: 18-month fixed-term contract from signing (running to at least January 2028), with a view to extending or moving to permanent as a second round of programme funding is confirmed.Location: Oxford, hybrid preferred, though we'd consider fully remote for the...
Ncounter Reading £120,000 – £140,000 pa
Hands-on engineering leadership role overseeing a multidisciplinary team of software, platform and DevOps engineers. You'll own technical architecture and standards across cloud-native applications, AI/ML platforms and MLOps, while remaining close to delivery and shaping longer-term technical strategy for National Security and Defence customers.
The locations with the most live listings for this role today.
Essential skills include proficiency in programming languages like Python, knowledge of data engineering and DevOps tools, and a strong understanding of machine learning concepts.
MLOps Engineers collaborate closely with data scientists to ensure that models are efficiently deployed and monitored in production. They work together to optimise the entire model lifecycle.
Key challenges include managing model versioning, ensuring model reproducibility, and scaling infrastructure to handle large datasets and high traffic loads.
Career progression typically starts with junior roles, advancing to senior and principal levels, and eventually leading to leadership positions in MLOps and AI.
Salary ranges can vary widely based on experience and location. For more detailed information, please refer to the salary section on this page.
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