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MLOps Engineer Jobs

Specialists who build and maintain the infrastructure that powers machine learning models. A critical role in scaling AI from research to production.

Open roles
4
Hiring companies
4
View 4 live roles → Get an alert

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.

What the role does

Inside the role of an MLOps Engineer

A typical week is split between developing and maintaining infrastructure, monitoring model performance, and collaborating with data scientists and engineers.

  1. 01
    Design and implement data pipelines for model training and inference.
  2. 02
    Develop and maintain CI/CD pipelines for model deployment.
  3. 03
    Monitor and optimise model performance in production.
  4. 04
    Collaborate with data scientists to integrate models into production systems.
  5. 05
    Troubleshoot and resolve issues in the MLOps infrastructure.
  6. 06
    Document processes and best practices for MLOps workflows.
Skills & tools

What hiring managers ask for

% of 4 listings posted in the last 12 months that mention each skill, extracted from job descriptions.

MLOps
75%
Python
75%
Machine Learning
75%
AWS
50%
Kubernetes
50%
Docker
50%
CI/CD
50%
AI
25%
Terraform
25%
DevOps
25%
Bash
25%
GitLab
25%
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

    Assist in setting up and maintaining data pipelines and CI/CD workflows. Work under supervision to support model deployment and monitoring.

  2. Level 2

    MLOps Engineer

    2–5 yrs

    Own the development and maintenance of MLOps infrastructure. Lead the integration of models into production systems and ensure smooth deployment processes.

  3. Level 3

    Senior MLOps Engineer

    5–8 yrs

    Oversee the entire MLOps lifecycle, from data ingestion to model monitoring. Mentor junior engineers and drive best practices in MLOps.

  4. Level 4

    Principal MLOps Engineer

    8+ yrs

    Strategise and lead the MLOps function, driving innovation and efficiency. Influence organisational MLOps standards and mentor senior engineers.

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

    Start by gaining a solid understanding of data engineering, DevOps, and machine learning fundamentals. Familiarise yourself with tools like Docker, Kubernetes, and TensorFlow.

  2. 2

    Build Projects

    Work on personal or open-source projects to gain hands-on experience with MLOps. Develop and deploy machine learning models to production environments.

  3. 3

    Gain Industry Experience

    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.

  4. 4

    Specialise in MLOps

    Focus on advanced MLOps topics such as automated model retraining, model versioning, and scalable infrastructure. Contribute to the MLOps community through blogs and talks.

  5. 5

    Lead MLOps Teams

    Take on leadership roles, managing MLOps teams and driving strategic initiatives. Develop and implement best practices for MLOps in your organisation.

  6. 6

    Influence the Field

    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.

Live jobs

4 live roles

MLOps Engineer

As an MLOps Engineer, you will be responsible for designing and maintaining the cloud infrastructure and CI/CD pipelines that support our AI-driven products. Your key responsibilities include implementing model monitoring, building robust ML pipelines, and ensuring compliance with data governance and security standards.

Morson Edge London, United Kingdom £700 – £800 pd
Hybrid Contract

MLOps Engineer / Machine Learning Engineer

This role involves building and maintaining MLOps frameworks to deploy, scale, and monitor machine learning models in highly secure, air-gapped environments. You'll work across engineering and data science teams to develop automated CI/CD pipelines, containerised applications, and production-grade ML systems using tools like GitLab, Docker, Kubernetes, MLflow, and Airflow. The role supports critical public sector digital transformation programmes with a focus on security and scalability.

Hays Technology Portsmouth, Hampshire, United Kingdom £537 pd
Hybrid Contract Clearance Required

AI Platform Engineer (DevOps / MLOps Focus)

We're hiring an experienced AI Platform Engineer to design, build and operate a production-grade Generative AI platform powering next-generation intelligent products. This is a hands-on engineering role focused on taking AI solutions from prototype to scalable, reliable services used in...

The Portfolio Group London, United Kingdom
Permanent

Senior / Lead Machine Learning Engineer - Time-Series, Pricing, MLOps

This role involves building and maintaining large-scale machine learning systems for forecasting, recommendation, pricing, and decision-making. You will work on time-series forecasting, scalable ML systems, feature stores, and MLOps, with a focus on technical depth and leadership within a high-calibre AI team.

Avanti Recruitment Paddington, London, W2 1HU, United Kingdom £90,000 – £120,000 pa
Hybrid Permanent
Hiring locations

Where this role is hiring

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

London

Location
2 open

Portsmouth

Location
1 open
FAQs

Common questions

  • 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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