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MLOps Engineer Jobs

Engineers who build and maintain the infrastructure that powers machine learning models. A critical role in scaling ML from prototype to production.

Open roles
4
Salary range
£35k – £106k
Hiring companies
4
View 4 live roles → Get an alert

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.

What the role does

Inside the role of an MLOps Engineer

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.

  1. 01
    Design and implement CI/CD pipelines for ML models.
  2. 02
    Monitor and troubleshoot production ML systems.
  3. 03
    Collaborate with data scientists to optimise model performance.
  4. 04
    Ensure data integrity and consistency across systems.
  5. 05
    Document and maintain infrastructure and deployment processes.
  6. 06
    Participate in code reviews and team meetings.
Salary on the board

£35k – £106k

Based on advertised midpoints across the 8 UK listings priced in pounds in the last 12 months. Base salary only.

Salary visibility
5% of listings advertise a salary — up from 0% the year before.
Skills & tools

What hiring managers ask for

% of 12 listings posted in the last 12 months that mention each skill, extracted from job descriptions.

Python
100%
MLOps
92%
Kubernetes
75%
Machine Learning
58%
CI/CD
50%
Bash
50%
Linux
50%
AI
42%
Infrastructure as Code
42%
Deep Learning
42%
Distributed Training
42%
Inference Optimization
42%
Career ladder

From Junior to Principal

A typical UK progression for mlops engineers. Years are guidance — strong people move faster, and many senior folks sidestep into research, product or management.

  1. Level 1

    Junior MLOps Engineer

    0–2 yrs

    Assists in the development and maintenance of ML infrastructure. Focuses on learning best practices and contributing to smaller projects.

  2. Level 2

    MLOps Engineer

    2–5 yrs

    Takes ownership of specific components of the ML infrastructure. Works on medium-sized projects and collaborates closely with data scientists and DevOps teams.

  3. Level 3

    Senior MLOps Engineer

    5–8 yrs

    Leads the design and implementation of complex ML infrastructure. Mentors junior engineers and drives best practices across the team.

  4. Level 4

    Principal MLOps Engineer

    8+ yrs

    Strategises and oversees the entire ML infrastructure. Influences company-wide ML practices and leads large-scale initiatives.

Pathway

How to become a MLOps Engineer

There's no single route, but most people follow some version of these steps.

  1. 1

    Learn the Basics

    Gain foundational knowledge in machine learning, DevOps, and software engineering. Familiarise yourself with tools like Docker, Kubernetes, and CI/CD pipelines.

  2. 2

    Build Practical Skills

    Work on small projects to develop hands-on experience with ML infrastructure. Contribute to open-source projects or personal projects to build a portfolio.

  3. 3

    Specialise in MLOps

    Focus on MLOps-specific tools and techniques. Learn about model versioning, data lineage, and monitoring. Start contributing to larger projects.

  4. 4

    Lead Projects

    Take ownership of significant components of the ML infrastructure. Collaborate with cross-functional teams to deliver robust and scalable solutions.

  5. 5

    Mentor and Influence

    Mentor junior engineers and drive best practices within the team. Influence company-wide ML strategies and contribute to the broader MLOps community.

  6. 6

    Strategise and Innovate

    Lead the development of cutting-edge ML infrastructure. Innovate to solve complex problems and drive the future of MLOps in your organisation.

Live jobs

4 live roles

Harnham - Data and Analytics Recruitment

MLOps Engineer

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  • London
  • Hybrid
  • £75,000 – £95,000 pa
  • Senior

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.

Dagster · Airflow · Prefect +3

Yesterday

Adria Solutions

Junior DevOps / MLOps Engineer

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  • Huddersfield
  • Hybrid
  • £35,000 – £45,000 pa
  • Junior

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.

AWS · GitHub · GitHub Actions +5

Last month

CMC Markets

ML Ops Engineer

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  • London
  • Mid-level

Hands-on role building the systems around machine learning: repeatable training and deployment workflows, CI/CD pipelines, model registries and versioning, plus batch and online inference services in containerised cloud environments. You'll monitor service health, data quality, drift and model performance, and investigate production issues across models, infrastructure and data dependencies, working with research, platform, security and product teams.

PyTorch · Model Registry · Feature Store +5

6 days ago

Cohere

Software Engineer, ML Ops

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  • London
  • Remote
  • Permanent

Owns the internal developer platform used by AI researchers to write, build, and debug training code, covering dev environments, container images, CI/CD, and training instrumentation. Works closely with researchers and ML engineers on stability, scalability, and observability, with a compensated 24x7 on-call rotation for production troubleshooting.

Kubernetes · Python · Go +2

5 days ago
Hiring locations

Where this role is hiring

The locations with the most live listings for this role today.

London

Location
3 open
FAQs

Common questions

  • 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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