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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
5
Hiring companies
4
View 5 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.
Skills & tools

What hiring managers ask for

% of 3 listings posted in the last 12 months that mention each skill, extracted from job descriptions.

MLOps
67%
Python
67%
Kubernetes
67%
Machine Learning
67%
AI
33%
AWS
33%
Terraform
33%
DevOps
33%
Time-Series
33%
Forecasting
33%
Recommendation Systems
33%
Feature Stores
33%
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

5 live roles

AI MLOps Engineer

This role involves working remotely as an MLOps Engineer for a growing pharmaceutical company. Responsibilities include implementing and maintaining machine learning operations, using technologies like AI, AWS, Python, Terraform, and Kubernetes. The position is a long-term contract outside IR35.

Morson Edge Peninsula, London, United Kingdom £600 – £650 pd
Remote Contract

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 AI-driven decision-making. You will work on time-series forecasting, scalable ML systems, feature stores, and MLOps, with a focus on technical depth and hands-on engineering. The team is small but high-calibre, offering significant ownership and the chance to influence technical direction.

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

Senior / Lead Machine Learning Engineer - TimeSeries, Pricing, Recommendation Engines, MLOps

This role involves building and maintaining large-scale machine learning systems for forecasting, pricing, and recommendation engines. You will work in a high-calibre AI team, focusing on time-series forecasting, feature stores, and MLOps, with opportunities to influence technical decisions and mentor junior engineers.

Avanti W21Ah, W2 1AH, United Kingdom £90,000 – £120,000 pa
Hybrid Permanent
Isomorphic Labs logo

Senior Software Engineer (ML Ops), London

As a Senior or Principal Software Engineer, you will lead the development and maintenance of a robust and scalable AI infrastructure, focusing on platform reliability, accelerator infrastructure, workload orchestration, and logging systems. You will work closely with research and applied ML teams to ensure the stability and performance of the systems that drive groundbreaking biotech innovations.

Isomorphic Labs London, United Kingdom
On-site Permanent
Hiring locations

Where this role is hiring

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

London

Location
2 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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