Machine Learning Infrastructure Engineer (Python, AWS)

About the role

Our client is looking for a Machine Learning Infrastructure Engineer (Python, AWS) to keep their pricing engine running reliably in production. Your focus is the machinery around the models: the pipelines that train and run them, the infrastructure they run on, and the monitoring that tells us when a model or a daily pricing run has gone wrong. This is a hands-on contributor role in a small, distributed team where you will see well-defined pieces of work through while actively building out model performance monitoring and observability.

What you'll do

• Building and maintaining the containerised pipelines that train models and produce daily price recommendations, along with the AWS scheduling and queueing that drives them.
• Watching daily pricing runs across every customer: catching failed or degraded runs, working through dead-letter queues, and getting a run back on track before it affects a customer's prices.
• Building out model performance monitoring — tracking prediction accuracy over time, input data drift, and how recommendations compare to what was actually sold.
• Extending the automated checks that stop a bad training run reaching production.
• Maintaining the model registry: versioned artefacts, and the assignments that decide which model version serves which customer.
• Owning CI/CD for the ML repository — build, test, package and release across a monorepo of independently versioned packages.

Requirements

• Commercial experience as a machine learning engineer, MLOps engineer, data engineer, or backend engineer working closely with ML systems in production.
• Strong Python · modern, type-annotated code, properly packaged and tested.
• Experience running things in production on AWS: containers, scheduled or event-driven jobs, queues, and object storage.
• Infrastructure as code (Terraform, OpenTofu or CDK) and CI/CD pipelines.
• Practical monitoring and observability experience — logs, metrics, alerting, and the judgement to know what is worth alerting on.
• Solid SQL and relational data skills.

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