Machine Learning Engineer

SPG Resourcing; York and North Yorkshire; Hybrid; GBP 70,000 - 95,000; Full time

Benefits offered by this job

Pension contributions

Generous annual leave

Flexible or hybrid working

Professional development

Job summary

SPG Resourcing is sourcing a Machine Learning Engineer for a Global Insurance Firm with offices in York and Manchester, working in a hybrid model. The role focuses on deploying, monitoring, and maintaining machine learning models at scale in production environments.

You will build APIs, deploy pipelines, and collaborate with data scientists and platform engineers to ensure scalable, reliable ML services across business applications.

Qualifications

• Bachelor’s or Master’s degree in a quantitative discipline or equivalent practical experience.
• Hands-on experience deploying, monitoring, and maintaining ML models in production.
• Strong Python development experience in ML engineering context.
• Solid software engineering practices including clean code and testing.
• Experience with cloud platforms (AWS/Azure/GC) and Docker.
• Experience implementing CI/CD pipelines and Git workflows.
• Knowledge of API development, monitoring, and logging.
• Ability to work independently in an agile environment.

Responsibilities

• Build and maintain infrastructure to deploy ML models in real-time and batch environments.
• Develop Python-based APIs to serve ML models in production systems.
• Collaborate with cross-functional teams to integrate ML services into applications.
• Ensure deployments follow best practices for scalability and reliability.
• Design and implement CI/CD pipelines for ML deployment.
• Monitor cloud-based ML services for performance and reliability.
• Contribute to code quality via code reviews and testing standards.
• Write clean, reusable code with OO principles and unit tests.
• Support data modelling and cloud infrastructure tasks as needed.
• Help develop and maintain the model registry including tracking and lifecycle management.

Skills

Python development

API development

CI/CD pipelines

Cloud platforms

Docker

Git workflows

Unit testing

Object-oriented programming

SQL

Monitoring/logging

Education

Bachelor’s or Master’s degree in Computer Science, Mathematics, Statistics, Engineering

Tools

Terraform

AWS/Azure/Google Cloud

Job description

Machine Learning Engineer - Global Insurance Firm

York | Manchester | Hybrid

Company Overview

Our client is a global specialist organisation operating within the insurance and financial services sector, recognised for its innovative approach to data, analytics, and emerging technologies. The organisation has a strong reputation for leveraging advanced data capabilities to drive smarter decision-making and deliver high-quality solutions to its customers.

With continued investment in artificial intelligence, cloud technologies, and analytics platforms, the company is building a modern data ecosystem that supports scalable machine learning solutions. The business fosters a collaborative and forward-thinking culture where engineering, data science, and technology teams work closely together to deliver impactful solutions across the organisation.

Job Summary

Our client is seeking aMachine Learning Engineer to support the development, deployment, and operational management of machine learning models within a growing data and analytics function.

In this hands‑on engineering role, you will build and maintain the infrastructure that enables machine learning models to operate at scale in production environments. You will work closely with data scientists, platform engineers, and developers to ensure machine learning solutions are robust, scalable, and seamlessly integrated into business applications.

The successful candidate will combine strong software engineering skills with an understanding of machine learning concepts, helping bridge the gap between model development and production deployment. You will play a key role in building APIs, developing deployment pipelines, and implementing best practices across the machine learning lifecycle.

Key Responsibilities

• Develop and maintain infrastructure to deploy machine learning models in both real-time and batch environments.
• Build and maintain Python-based APIs to serve machine learning models in production systems.
• Collaborate with cross-functional engineering teams to integrate machine learning services into applications and platforms.
• Work with platform and infrastructure teams to ensure deployments follow best practices for scalability and reliability.
• Design and implement CI/CD pipelines to support efficient machine learning model deployment.
• Monitor and maintain cloud-based machine learning services to ensure performance and reliability.
• Contribute to code quality through pull request reviews and adherence to software engineering standards.
• Write maintainable and reusable code using object-oriented programming principles and unit testing.
• Support data modelling and cloud infrastructure tasks where required.
• Contribute to the development and maintenance of the organisation’s model registry, including model tracking, monitoring, and lifecycle management.

Required

• Bachelor’s or Master’s degree in a quantitative discipline such as Computer Science, Mathematics, Statistics, Engineering, or a related field, or equivalent practical experience.
• Hands‑on experience deploying, monitoring, and maintaining machine learning models in production environments.
• Strong Python development experience, ideally within a machine learning engineering context.
• Solid understanding of software engineering best practices, including clean code principles and testing.
• Experience working with cloud platforms such as AWS, Azure, or Google Cloud.
• Familiarity with containerisation technologies such as Docker.
• Experience implementing CI/CD pipelines and working with Git-based development workflows.
• Knowledge of API development, monitoring, and logging.
• Strong problem‑solving abilities and the ability to work independently on technical tasks.
• Experience working within Agile development environments.

Preferred

• Experience working within financial services or insurance environments.
• Familiarity with Infrastructure as Code tools such as Terraform.
• Experience implementing Test Driven Development (TDD) methodologies.
• Knowledge of cloud networking and distributed infrastructure.
• Experience working with a variety of machine learning models such as neural networks, ensemble methods, or other advanced techniques.

Key Technical Skills

• Python (including Flask or FastAPI frameworks)
• Object-Oriented Programming and unit testing
• Machine learning model deployment and management
• Terraform or similar Infrastructure-as-Code tools
• Cloud platforms (AWS, Azure, or Google Cloud)
• Docker and containerised deployments
• Git-based development workflows
• SQL and data querying
• Cloud and API monitoring tools
• Competitive salary and performance-related incentives
• Pension contributions
• Generous annual leave allowance
• Flexible or hybrid working arrangements
• Professional development and training opportunities
• Collaborative and innovative working culture
• Opportunity to work on advanced machine learning and AI-driven initiatives

Equal Opportunity Statement

SPG Resourcing is an equal opportunities employer and is committed to fostering an inclusive workplace which values and benefits from the diversity of the workforce we hire. We offer reasonable accommodation at every stage of the application and interview process.

Global Insurance Firm | York | Manchester | Hybrid