Machine Learning Engineer
DATA & MACHINE LEARNING | FINANCIAL SERVICES
Most machine learning roles focus on building models. This one is about making sure they actually work in production.
You'll join a newly formed ML Engineering team within a large, established financial services business. The company already has data scientists developing models. It now needs the engineering capability to deploy them properly, monitor them and make them usable across the organisation.
You'll build the infrastructure that takes models from research code through to reliable production services across Azure and GCP.
What you’ll work on
This is a hands-on engineering role. You'll:
- Build Python APIs using FastAPI or Flask to serve machine learning models.
- Deploy models in real-time and batch environments.
- Develop CI/CD pipelines that automate model testing and deployment.
- Help automate the full ML lifecycle, from dataset creation and training through to evaluation, deployment and monitoring.
- Build and improve the company's model registry.
- Monitor production ML services and manage model upgrades and retirement.
- Use Terraform and Docker to create scalable, repeatable infrastructure.
- Work with data scientists to turn research code into maintainable production software.
- Collaborate with data, platform and application engineers to integrate ML services into products used across the business.
You won't be handed models and asked to deploy them blindly. You'll be expected to understand how they work, question decisions where necessary and help determine the right way to operate them in production.
What we’re looking for
You'll probably have around three to five years’ experience in machine learning engineering, including direct responsibility for deploying and maintaining models in production.
You should be comfortable with:
- Production-level Python, including OOP, unit testing and TDD.
- FastAPI or Flask.
- ML deployment, monitoring and model lifecycle management.
- Azure, GCP or AWS.
- Terraform or another infrastructure-as-code tool.
- Docker, CI/CD and Git-based development.
- API monitoring, logging and production support.
- Working with models such as neural networks and random forests.
Financial services or insurance experience would be useful, but it isn't essential. Strong ML and software engineering fundamentals matter more.
Why consider it?
The ML Engineering team is new, but it sits within an established technology function responsible for around 140 applications.
That gives you an unusual combination: genuine greenfield work, backed by an organisation with the data, investment and real-world use cases needed to put machine learning into production at scale.
You’ll have a meaningful say in how the deployment framework, model registry and wider MLOps capability are built. You won't simply inherit someone else's setup.
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