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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
2
Hiring companies
2
View 2 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 1 listings posted in the last 12 months that mention each skill, extracted from job descriptions.

Kubernetes
100%
Terraform
100%
AWS
100%
Azure
100%
GCP
100%
Docker
100%
CI/CD
100%
Infrastructure as Code
100%
Observability
100%
Cloud-Native
100%
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

2 live roles

Platform Engineer (DevOps / MLOps Focus)

This role involves designing and maintaining cloud-native infrastructure and Kubernetes environments to support large-scale AI and machine learning workloads. You'll use Terraform for Infrastructure as Code and collaborate closely with engineering and data science teams to optimise deployment workflows and platform performance. The position focuses on building scalable, secure, and observable platforms for AI in a high-visibility environment.

The Portfolio Group London, United Kingdom £100,000 pa
Hybrid Permanent

Senior Software Engineer 1 (MLOps)

We’re Aioi R&D Lab - an AI tech hub in one of the fastest-growing insurance companies. We research and develop AI systems that catapult insurance from a slow-moving, traditional past into a data-driven, technology-lead and society-defining future.We’re looking for dynamic,...

Aioi Nissay Dowa Europe Oxford, Oxfordshire, United Kingdom £81,322 – £112,488 pa
Hiring locations

Where this role is hiring

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

London

Location
1 open

Oxford

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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