Machine Learning Engineer

SPG Resourcing; Manchester; On-site; GBP 70,000 - 110,000; Full time

Job summary

SPG Resourcing on behalf of a leading financial services organisation is recruiting a Machine Learning Engineer to take models from research to production within a collaborative data function in Manchester.

You will design scalable ML workflows, deploy in cloud-native environments using Docker, Kubernetes and CI/CD, and work closely with Data Scientists and Engineers to deliver production-ready AI solutions.

Qualifications

• Commercial experience in ML engineering or data science within production.
• Strong Python development with software engineering best practices.
• Experience deploying ML solutions in cloud-native production environments.
• Experience with Docker and Kubernetes.
• Knowledge of MLOps: CI/CD, Git, infrastructure automation.
• Experience with Azure and Databricks is beneficial.
• Experience with LLMs or Generative AI in a commercial environment is desirable.
• Experience deploying ML in regulated industries such as financial services.

Responsibilities

• Design and enhance the organisation's ML engineering capability and Data Science platform.
• Build and automate end-to-end ML workflows using CI/CD and Infrastructure as Code.
• Collaborate with Data Scientists through model development and deployment lifecycle.
• Work with engineering teams and stakeholders to deliver production-ready AI solutions.
• Develop high-quality Python code following software engineering best practices.
• Contribute to design decisions including deployment strategies and solution architecture.
• Support deployment and operationalisation of traditional ML and Generative AI solutions.
• Help establish engineering standards, tooling and best practices as the function grows.

Skills

Python development

Cloud deployments

MLOps

CI/CD

Agile delivery

Communication skills

Git / version control

Tools

Docker

Kubernetes

Azure

Databricks

Azure DevOps

Jira

Job description

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