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# Machine Learning Engineer

- **Company:** Method Resourcing
- **Location:** York, UK
- **Experience:** Experienced
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), Amazon Web Services, Artificial Neural Networks, Unit Testing, Microsoft Azure, Continuous Integration, Python (Programming Language), Machine Learning, Object-Oriented Software Development, Azure Machine Learning, Data Logging, Test-Driven Development (TDD), Flask (Web Framework), Git, Fastapi, Machine Learning Operations, Terraform, Docker
- **Published:** August 7, 2026
- **Apply:** https://www.totaljobs.com/job/machine-learning-engineer/method-resourcing-job107807471

## About the Role

You'll probably have around three to five years' experience in machine learning engineering, including direct responsibility for deploying and maintaining models in production.

You should be comfortable with:

 * Production-level Python, including OOP, unit testing and TDD.
 * FastAPI or Flask.
 * ML deployment, monitoring and model lifecycle management.
 * Azure, GCP or AWS.
 * Terraform or another infrastructure-as-code tool.
 * Docker, CI/CD and Git-based development.
 * API monitoring, logging and production support.
 * Working with models such as neural networks and random forests.

Financial services or insurance experience would be useful, but it isn't essential. Strong ML and software engineering fundamentals matter more.

## Description

Most machine learning roles focus on building models. This one is about making sure they actually work in production.

You'll join a newly formed ML Engineering team within a large, established financial services business. The company already has data scientists developing models. It now needs the engineering capability to deploy them properly, monitor them and make them usable across the organisation.

You'll build the infrastructure that takes models from research code through to reliable production services across Azure and GCP.

What you'll work on

This is a hands-on engineering role. You'll:

 * Build Python APIs using FastAPI or Flask to serve machine learning models.
 * Deploy models in real-time and batch environments.
 * Develop CI/CD pipelines that automate model testing and deployment.
 * Help automate the full ML lifecycle, from dataset creation and training through to evaluation, deployment and monitoring.
 * Build and improve the company's model registry.
 * Monitor production ML services and manage model upgrades and retirement.
 * Use Terraform and Docker to create scalable, repeatable infrastructure.
 * Work with data scientists to turn research code into maintainable production software.
 * Collaborate with data, platform and application engineers to integrate ML services into products used across the business.

You won't be handed models and asked to deploy them blindly. You'll be expected to understand how they work, question decisions where necessary and help determine the right way to operate them in production.


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