About this role
Salary: £70,000 - 70,000 per year
Requirements: We are looking for around three to five years experience in machine learning engineering, including direct responsibility for deploying and maintaining models in production.We need strong production-level Python skills, including OOP, unit testing and TDD.We expect experience with FastAPI or Flask.We value experience in ML deployment, monitoring and model lifecycle management.We need familiarity with Azure, GCP or AWS.We expect experience with Terraform or another infrastructure-as-code tool.We need experience with Docker, CI/CD and Git-based development.We value experience with API monitoring, logging and production support.We expect understanding of working with models such as neural networks and random forests.Financial services or insurance experience would be useful, but it is not essential.Strong ML and software engineering fundamentals are more important than industry background. Responsibilities: We build Python APIs using FastAPI or Flask to serve machine learning models.We deploy models in real-time and batch environments.We develop CI/CD pipelines that automate model testing and deployment.We help automate the full ML lifecycle, from dataset creation and training through to evaluation, deployment and monitoring.We build and improve our model registry.We monitor production ML services and manage model upgrades and retirement.We use Terraform and Docker to create scalable, repeatable infrastructure.We work with data scientists to turn research code into maintainable production software.We collaborate with data, platform and application engineers to integrate ML services into products used across the business.We understand how models work, question decisions where necessary and help determine the right way to operate them in production. Technologies: APIAWSAzureCI/CDDockerFastAPIFlaskGCPGitSupportMachine LearningMLOpsOOPPythonTDDTerraformCloudPLC More:
We are a newly formed ML Engineering team within a large, established financial services business. Our wider technology function is responsible for around 140 applications, giving this role a strong mix of greenfield ownership and the backing of an organisation with the data, investment and real-world use cases needed to put machine learning into production at scale. We are building the infrastructure, deployment framework, model registry and wider MLOps capability that will take models from research code through to reliable production services across Azure and GCP. This is a hands-on role with real influence over how our machine learning capability is shaped and operated.
last updated 32 week of 2026