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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
10
Salary range
£80k – £116k
Hiring companies
4
View 10 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

£80k – £116k

Based on advertised midpoints across the 3 priced listings posted in the last 12 months. Base salary only.

Salary visibility
3% of listings advertise a salary — up from 0% the year before.
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%
AWS
67%
Python
67%
Terraform
67%
Kubernetes
67%
Docker
67%
CI/CD
67%
AI
33%
DevOps
33%
Machine Learning
33%
Azure
33%
FastAPI
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

10 live roles

See all 10 roles →

MLOps Engineer

Design and operate a secure, scalable AI platform infrastructure, building CI/CD pipelines and deployment workflows in collaboration with data science and engineering teams. Implement observability, automate infrastructure using IaC, and ensure high availability and compliance. Focus on performance, cost efficiency, and seamless integration of AI workloads and agents.

DGH Recruitment London, City And County Of the City Of London, United Kingdom £80,000 – £100,000 pa

MLOps Engineering Manager

Lead the design, migration, and delivery of scalable ML systems while managing a team of 5-10 engineers. Drive technical strategy in a cloud-native environment during a key transition from MLflow to AWS SageMaker, supporting predictive maintenance and component lifecycle optimisation. Balance hands-on engineering with people leadership in a hybrid setup involving regular collaboration with data scientists and stakeholders.

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

Senior MLOps Engineer

Develop and maintain MLOps infrastructure to productionise advanced AI and computer vision models, from distributed training to deployment on cloud and GPU-accelerated edge devices. Work closely with research and engineering teams to build scalable CI/CD pipelines, model registries, and monitoring systems. Operate in high-assurance environments with exposure to defence and security applications.

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 full-stack AI/ML systems for internal and external customers, focusing on MLOps pipelines, distributed training, and inference performance. Build open-source tools and reference architectures to scale AI workloads on NVIDIA platforms and cloud partners. Act as a technical advisor for complex production issues across hardware, software, and infrastructure layers.

NVIDIA £292,500 – £650,000 pa
Remote Permanent
NVIDIA logo

Senior MLOps Engineer - DSX Enablement

Develop and optimize full-stack AI/ML systems, focusing on MLOps pipelines, distributed training, and inference performance across cloud and on-prem environments. Collaborate with internal teams and external customers to solve complex infrastructure challenges, particularly around LLMs and new hardware integration. Build open-source tools and reference architectures to scale AI workloads effectively.

NVIDIA PLN 292,500 – PLN 650,000 pa

Platform Engineer (DevOps / MLOps Focus)

Design and maintain cloud-native infrastructure to support large-scale AI and machine learning workloads, with a focus on Kubernetes, Terraform, and platform automation. Collaborate with engineering and data science teams to optimise deployment workflows and improve platform reliability, scalability, and observability for production AI systems.

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

Machine Learning Engineer - Conversational AI & MLOps

Design and deploy low-latency, production-grade ASR, TTS, LLM, and SLM pipelines for real-time conversational AI applications. Build high-performance APIs with FastAPI and optimise GPU utilisation and cloud infrastructure across AWS, Azure, and GCP. Implement MLOps, CI/CD, and containerisation workflows to support scalable, cost-efficient model deployment and serving in cloud and edge environments.

Robert Walters London, United Kingdom £70,000 – £90,000 pa
NVIDIA logo

Senior MLOps Engineer - DSX Enablement

Develop and optimize full-stack AI/ML systems, focusing on MLOps pipelines, distributed training, and inference performance across NVIDIA's cloud platforms and open-source frameworks. Act as a technical advisor for internal and external customers, solving complex production issues and building scalable reference architectures. Work closely with infrastructure and framework teams to enhance AI workload capabilities on accelerated systems.

NVIDIA Germany PLN 292,500 – PLN 650,000 pa
See all 10 roles →
Hiring locations

Where this role is hiring

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

London

Location
1 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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