ML Ops Engineer | York

Oliver James
North Humberside, United Kingdom
Last month
Job Type
Permanent
Work Pattern
Full-time
Work Location
Hybrid
Seniority
Lead
Education
Degree
Posted
23 Apr 2026 (Last month)

Machine Learning Engineering Manager

We're building a new ML Engineering team and are looking for a strong technical lead to help take our machine learning capability from proof-of-concept to fully scaled, production-ready solutions.

Sitting within our Group & Enterprise Services (GES) function, this role is part of the Data vertical and reports into the Head of Data Engineering. You'll be hands-on with cloud infrastructure, APIs and deployment pipelines, working mainly in GCP Vertex AI (essential) and Azure (desirable). Your focus will be enabling data scientists to deploy high-impact models reliably and at scale.

You'll combine leadership, architectural thinking and deep engineering skills to shape the ML platform, coach engineers and deliver robust, enterprise-ready ML services.

What you'll do

* Lead, mentor and develop a small team of ML Engineers

* Oversee delivery of ML capabilities and support planning and capacity needs

* Shape architecture from early design through to production

* Build and maintain Python APIs (Flask/FastAPI) for model serving

* Develop infrastructure for real-time and batch deployments

* Design and maintain CI/CD pipelines for models

* Ensure code quality, engineering best practice and scalable cloud deployments

* Collaborate with data scientists, platform engineers and developers

* Support model lifecycle management, monitoring and automation

* Break down solution designs into deliverables and milestones

What you'll bring

* 5+ years as an ML Engineer with strong Python engineering skills

* Experience deploying and maintaining ML models in production (Vertex AI required)

* Strong software engineering fundamentals: OOP, unit testing, TDD

* Cloud experience (GCP, AWS or Azure) and IaC tools such as Terraform

* Experience with Docker, CI/CD pipelines and Git workflows

* Understanding of data science principles and taking research code to production

* Strong problem-solving skills and the ability to work independently

* Comfortable working in Agile teams

* Clear communication, collaboration and a proactive, improvement-driven mindset

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