Engineers who build and maintain the infrastructure that powers machine learning models. A critical role in scaling ML from prototype to production.
MLOps Engineers are the backbone of modern machine learning operations. They focus on building, maintaining, and optimising the infrastructure that enables data scientists and ML researchers to deploy models efficiently and at scale. This role is crucial in bridging the gap between development and production, ensuring that ML models are reliable, scalable, and performant. MLOps Engineers work closely with data scientists, DevOps teams, and software engineers to streamline the entire ML lifecycle, from data ingestion to model deployment and monitoring.
A typical week for an MLOps Engineer is a mix of coding, testing, and collaboration. They spend time developing and maintaining CI/CD pipelines, ensuring data integrity, and optimising model performance.
Based on advertised midpoints across the 9 UK listings priced in pounds in the last 12 months. Base salary only.
% of 11 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.
Assists in the development and maintenance of ML infrastructure. Focuses on learning best practices and contributing to smaller projects.
Takes ownership of specific components of the ML infrastructure. Works on medium-sized projects and collaborates closely with data scientists and DevOps teams.
Leads the design and implementation of complex ML infrastructure. Mentors junior engineers and drives best practices across the team.
Strategises and oversees the entire ML infrastructure. Influences company-wide ML practices and leads large-scale initiatives.
There's no single route, but most people follow some version of these steps.
Gain foundational knowledge in machine learning, DevOps, and software engineering. Familiarise yourself with tools like Docker, Kubernetes, and CI/CD pipelines.
Work on small projects to develop hands-on experience with ML infrastructure. Contribute to open-source projects or personal projects to build a portfolio.
Focus on MLOps-specific tools and techniques. Learn about model versioning, data lineage, and monitoring. Start contributing to larger projects.
Take ownership of significant components of the ML infrastructure. Collaborate with cross-functional teams to deliver robust and scalable solutions.
Mentor junior engineers and drive best practices within the team. Influence company-wide ML strategies and contribute to the broader MLOps community.
Lead the development of cutting-edge ML infrastructure. Innovate to solve complex problems and drive the future of MLOps in your organisation.
Harnham - Data and Analytics Recruitment London £75,000 – £85,000 pa
Own and develop the MLOps infrastructure behind a growing AI platform, building orchestration pipelines with Dagster, Airflow or Prefect and deploying ML workloads on Kubernetes and cloud. You will implement CI/CD, observability and monitoring, manage infrastructure as code with Terraform or Bicep, and work closely with Data Scientists and ML Engineers to scale production ML, NLP and AI systems.
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 with software and AI engineers to keep development, testing and production environments consistent, monitoring pipelines and fixing issues. Suits an early-career engineer with cloud, scripting and Git foundations.
Harnham - Data and Analytics Recruitment London £75,000 – £85,000 pa
Owns and evolves the platform infrastructure behind AI services, designing scalable MLOps systems and deploying models on Azure ML and AKS. Builds orchestration pipelines for training, inference, monitoring and retraining, plus CI/CD and observability tooling, while driving Responsible AI governance across engineering and consulting teams.
The locations with the most live listings for this role today.
Essential skills include proficiency in programming languages like Python, knowledge of DevOps tools (Docker, Kubernetes), and experience with CI/CD pipelines. Understanding of machine learning concepts and data engineering is also crucial.
Start by gaining experience in related fields such as DevOps, data engineering, or software development. Build a portfolio of MLOps projects and consider certifications in cloud platforms and ML frameworks.
Key challenges include managing model versioning, ensuring data integrity, and scaling infrastructure to handle large datasets. Monitoring and maintaining model performance in production is also a significant challenge.
The typical progression starts as a Junior MLOps Engineer, advancing to MLOps Engineer, then Senior MLOps Engineer, and finally Principal MLOps Engineer. Each level involves increasing responsibility and influence over the ML infrastructure.
Salaries for MLOps Engineers can vary widely based on experience, location, and company size. For specific salary ranges, please refer to the salary section on this page.
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