Machine Learning Engineer

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.

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.

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• 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.
  

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• 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.
  

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• 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.
  

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• 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 toolsCompetitive 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
  

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.