Machine Learning Engineer

About the role

Join a newly created Machine Learning Engineering team within a large financial services organisation. You will bridge the gap between data‑science research and production, ensuring models are reliable, scalable and deliver real business value.

Key responsibilities

• Design and implement Python APIs (FastAPI or Flask) to serve ML models.
• Deploy models in real‑time and batch environments on Azure and GCP.
• Build CI/CD pipelines that automate testing, deployment and monitoring of models.
• Create and maintain infrastructure using Terraform and Docker.
• Develop and manage a model registry and oversee model lifecycle, including upgrades and retirement.
• Collaborate with data scientists, platform engineers and application teams to turn research code into production‑ready services.

Required profile

• 3‑5 years of experience in machine‑learning engineering with a focus on production deployment.
• Strong understanding of production‑grade Python, OOP, unit testing and TDD.
• Experience working with cloud platforms (Azure, GCP or AWS) and infrastructure‑as‑code tools.
• Ability to question model decisions and contribute to robust operational practices.

Required skills

• Python
• FastAPI or Flask
• CI/CD pipelines
• Terraform
• Docker
• Azure, GCP or AWS
• Git
• Unit testing / Test‑Driven Development
• Model registry, monitoring and logging
• Neural networks, random forests

What we offer

• Permanent role with a competitive salary (£60,000‑£70,000 per year).
• Opportunity to shape a green‑field MLOps capability within an established technology function.
• Access to real‑world financial data and large‑scale production environments.