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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
10
Salary range
£77k – £118k
Hiring companies
6
View 10 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.
Salary on the board

£77k – £118k

Based on advertised midpoints across the 4 priced listings posted in the last 12 months. Base salary only.

Salary visibility
2% of listings advertise a salary — up from 0% the year before.
Skills & tools

What hiring managers ask for

% of 5 listings posted in the last 12 months that mention each skill, extracted from job descriptions.

Python
80%
Kubernetes
80%
AWS
60%
Terraform
60%
Docker
60%
CI/CD
60%
MLOps
40%
Observability
40%
Machine Learning
40%
GCP
40%
Azure
40%
AI
20%
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

10 live roles

See all 10 roles →

MLOps Engineering Manager

Lead and mentor a team of MLOps engineers while driving the architectural and technical delivery of machine learning systems, including a migration from MLflow to AWS SageMaker. Collaborate with data scientists and cross-functional teams to build scalable solutions for predictive maintenance, fault detection, and component lifecycle optimisation. Balance hands-on technical work with people leadership in a hybrid environment with regular office presence.

Uniting Ambition Ruislip Manor, London, United Kingdom £100,000 – £120,000 pa

Senior MLOps Engineer

This role involves building and maintaining MLOps infrastructure to productionise advanced AI and computer vision models, from distributed training to deployment on cloud and edge devices. The engineer will optimise inference, manage model registries, and develop CI/CD and monitoring pipelines while working closely with research and software teams. The position requires strong hands-on skills in Python, containerisation, and GPU-accelerated systems, particularly NVIDIA Jetson platforms.

MFK Recruitment Brentford, London, TW8 9DE, United Kingdom £75,000 – £100,000 pa
Hybrid Permanent Clearance Required
NVIDIA logo

Senior MLOps Engineer - DSX Enablement

Develop and optimize scalable AI/ML infrastructure solutions on NVIDIA platforms, supporting both internal and external customers. Focus on MLOps pipeline development, distributed training, inference optimization, and deep-stack performance tuning across hardware and software layers. Contribute to open-source tools and collaborate with engineering teams to enhance AI frameworks and cloud integrations.

NVIDIA Germany PLN 292,500 – PLN 650,000 pa
NVIDIA logo

Senior MLOps Engineer - DSX Enablement

This role involves building and deploying scalable AI solutions on cloud platforms, with a focus on MLOps pipelines, distributed training, and inference optimization. You'll act as a technical advisor for internal and external customers, solving complex full-stack AI system issues and contributing to open-source tools. The position emphasizes deep systems knowledge, performance tuning, and collaboration with infrastructure teams to support cutting-edge AI workloads.

NVIDIA PLN 292,500 – PLN 650,000 pa

MLOps Platform Developer / Full-Stack AI Engineer

This role involves end-to-end ownership of a full-stack engineering platform integrating AI, real-time telemetry, and building management systems. You'll operate and evolve an LLM serving stack with fine-tuning pipelines, maintain a React/TypeScript frontend with offline-first mobile capabilities, and manage large-scale PostgreSQL databases and MQTT-based data ingestion from IoT systems. The position combines MLOps, full-stack development, and domain-specific engineering intelligence in the context of energy transition infrastructure.

Anonymous London, United Kingdom

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
NVIDIA logo

Senior MLOps Engineer - DSX Enablement

This role involves building and deploying custom AI solutions on NVIDIA's NeoCloud and partner cloud platforms, with a focus on MLOps pipelines, distributed training, and inference optimization. The engineer will act as a technical advisor to both internal and external customers, diagnosing full-stack AI/ML system issues and contributing to open-source tools and reference architectures. A strong emphasis is placed on performance tuning, scalability, and deep collaboration with infrastructure and framework teams to advance AI capabilities.

NVIDIA
See all 10 roles →
Hiring locations

Where this role is hiring

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

London

Location
2 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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