MLOps Engineer: Real-Time AI in Production

Checkatrade; Barcelona; Hybrid; EUR 70,000 - 100,000; Full time

Benefits offered by this job

Competitive salary

Annual bonus

Equity scheme

Health & wellbeing

Gym membership

Cycle scheme

Birthday/leave

Job summary

Checkatrade is seeking an MLOps Engineer to own how machine learning and AI run in production. You will turn our Data Scientists’ models into live, reliable services, and build the secure foundations that let AI tools and agents use our data.

You’ll report to the Head of Data Engineering, in Barcelona (on-site) or London (hybrid). The role requires strong Python, production ML deployment, GCP experience, Docker and Kubernetes, CI/CD, and IaC.

Qualifications

• Proven Python used for ML deployment and monitoring.
• Experience deploying ML models in production environments.
• Ability to build observability for ML/data systems.
• Security-focused approach to data access and secrets management.
• Ownership mindset for production systems beyond launch.
• Strong collaboration with Data Scientists and engineers.

Responsibilities

• Deploy, automate, monitor and retrain ML models with real-time value.
• Set and uphold MLOps standards (CI/CD, versioning).
• Build infrastructure for access to Checkatrade data securely.
• Maintain and secure the GCP/AWS data platform and reduce debt.
• Monitor drift, costs and reliability of models and pipelines.
• Ensure secure, auditable ML workloads and reproducibility.

Skills

Python

Production ML deployment

Observability

Security-minded

Ownership of production systems

Collaboration with Data Scientists

Tools

GCP

AWS

Docker

Kubernetes

CI/CD

Infrastructure as Code

Job description

Checkatrade is seeking an MLOps Engineer to own how machine learning and AI run in production. You will turn our Data Scientists’ models into live, reliable services, and build the secure foundations that let AI tools and agents use our data.

You’ll report to the Head of Data Engineering, in Barcelona (on-site) or London (hybrid). The role requires strong Python, production ML deployment, GCP experience, Docker and Kubernetes, CI/CD, and IaC.