Machine Learning Engineer

Machine Learning Engineer

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.
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.
The Role
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.
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.
This role is ideal for someone with a passion for software engineering, cloud technologies and productionising machine learning solutions within an enterprise environment.
Key responsibilities
Design, develop and enhance the organisation's machine learning engineering capability and Data Science platform
Build and automate end-to-end machine learning workflows using CI/CD and Infrastructure as Code
Collaborate with Data Scientists throughout the model development and deployment lifecycle
Work closely with engineering teams and business stakeholders to deliver production-ready AI solutions
Develop high-quality, maintainable Python code following software engineering best practices
Contribute to technical design decisions including model deployment strategies and solution architecture
Support the deployment and operationalisation of both traditional machine learning and Generative AI solutions
Help establish engineering standards, tooling and best practices as the function continues to grow
Required skills and experience
Commercial experience in Machine Learning Engineering or Data Science within a production environment
Strong Python development skills with a solid understanding of software engineering best practices
Experience deploying machine learning solutions into cloud-native production environments
Experience with containerisation technologies such as Docker and orchestration platforms including Kubernetes
Knowledge of modern MLOps practices including CI/CD, version control (Git) and infrastructure automation
Experience working with cloud platforms and modern data ecosystems (Azure and Databricks experience beneficial)
Strong understanding of machine learning principles and model deployment processes
Excellent communication skills with the ability to explain technical concepts to non-technical stakeholders
Experience working with Agile delivery methodologies and tools such as Azure DevOps and Jira
Experience working with Large Language Models (LLMs), Generative AI or Agentic AI solutions in a commercial environment
Experience deploying machine learning models within regulated industries such as financial services or insurance
Exposure to enterprise-scale MLOps and cloud infrastructure
Experience contributing to platform architecture and engineering best practices

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