Machine Learning Engineer

Job Description

Machine Learning Engineer

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Location: Flexible (Hybrid)

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Working set up: Hybrid

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Salary: Competitive + bonus + benefits

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SPG are working on behalf of an established financial services organisation investing heavily in its data science and AI capabilities. As part of an expanding team, the business is delivering a range of greenfield machine learning and generative AI initiatives designed to solve real-world business challenges and enhance customer outcomes.

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This is an exciting opportunity to join a collaborative data function where you'll help shape the organisation's machine learning engineering capability while building scalable, production-ready AI solutions.

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The Role

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Working as part of a cross-functional Data Science team, the Machine Learning Engineer will play a key role in taking machine learning models from research through to production.

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You'll work closely with Data Scientists, Data Engineers and Software Engineers to build robust, scalable ML solutions while helping define best practices, tooling and automation across the full machine learning lifecycle.

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This role is ideal for someone with a passion for software engineering, cloud technologies and productionising machine learning solutions within an enterprise environment.

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Key responsibilities

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• Design, develop and enhance the organisation's machine learning engineering capability and Data Science platform

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• Build and automate end-to-end machine learning workflows using CI/CD and Infrastructure as Code

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• Collaborate with Data Scientists throughout the model development and deployment lifecycle

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• Work closely with engineering teams and business stakeholders to deliver production-ready AI solutions

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• Develop high-quality, maintainable Python code following software engineering best practices

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• Contribute to technical design decisions including model deployment strategies and solution architecture

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• Support the deployment and operationalisation of both traditional machine learning and Generative AI solutions

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• Help establish engineering standards, tooling and best practices as the function continues to grow

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Required skills and experience

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• Commercial experience in Machine Learning Engineering or Data Science within a production environment

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• Strong Python development skills with a solid understanding of software engineering best practices

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• Experience deploying machine learning solutions into cloud-native production environments

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• Experience with containerisation technologies such as Docker and orchestration platforms including Kubernetes

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• Knowledge of modern MLOps practices including CI/CD, version control (Git) and infrastructure automation

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• Experience working with cloud platforms and modern data ecosystems (Azure and Databricks experience beneficial)

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• Strong understanding of machine learning principles and model deployment processes

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• Excellent communication skills with the ability to explain technical concepts to non-technical stakeholders

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• Experience working with Agile delivery methodologies and tools such as Azure DevOps and Jira

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Desirable experience

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• Experience working with Large Language Models (LLMs), Generative AI or Agentic AI solutions in a commercial environment

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• Experience deploying machine learning models within regulated industries such as financial services or insurance

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• Exposure to enterprise-scale MLOps and cloud infrastructure

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• Experience contributing to platform architecture and engineering best practices

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