MLOps Engineer: Production ML Pipelines & Cloud

Hiscox Underwriting Group Services Ltd (HUGS); York and North Yorkshire; On-site; GBP 70,000 - 100,000; Full time

Job summary

Hiscox is seeking an experienced Machine Learning Engineer to join a newly formed ML Engineering team. You will build and maintain infrastructure to deploy data‑driven models, and develop Python APIs to serve ML models in production within Azure, AWS, or GCP.

You’ll collaborate with Data Scientists, Platform Engineers and Developers to ensure scalable, reliable services. The role focuses on production‑grade ML pipelines, model registry improvements, and CI/CD for data science workloads in an

Qualifications

• Hands-on experience deploying and maintaining ML models in production.
• Strong Python development with Flask/FastAPI.
• Experience with cloud platforms and CI/CD.

Responsibilities

• Develop and maintain infrastructure for deploying ML models in real‑time and batch environments.
• Build and maintain Python APIs (Flask/FastAPI) to serve ML models.
• Collaborate with cross discipline engineers to integrate ML services into user‑facing applications.
• Work with platform engineers to align with infrastructure best practices and ensure scalable deployments.
• Review pull requests and contribute to code quality across the MLE team.
• Monitor and maintain cloud‑based ML services, ensuring reliability and performance.

Skills

Python development

Flask/FastAPI

OOP

Unit testing

Agile methods

Problem solving

Education

Bachelor's/Master's degree in CS/Math/Engineering

Tools

Terraform

Docker

Kubernetes

CI/CD pipelines

Git workflows

Cloud platforms (GCP/AWS/Azure)

Job description

Hiscox is seeking an experienced Machine Learning Engineer to join a newly formed ML Engineering team. You will build and maintain infrastructure to deploy data‑driven models, and develop Python APIs to serve ML models in production within Azure, AWS, or GCP.

You’ll collaborate with Data Scientists, Platform Engineers and Developers to ensure scalable, reliable services. The role focuses on production‑grade ML pipelines, model registry improvements, and CI/CD for data science workloads in an