WFH Machine Learning Infrastructure Engineer (Python, AWS)

Overview 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. Schedule: Monday to Friday, 9:00 AM to 6:00 PM Adelaide SA time, includes a 1-hour unpaid break(8 hours per day/40 hours per week) Responsibilities 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. Writing and maintaining the Terraform / OpenTofu that defines the ML infrastructure. Supporting the team's modelling work: preparing data, reproducing results, and productionising experiments once they graduate. Improving the runtime and cost of training and inference jobs. Tech Stack Backend: Python, FastAPI, Node.js, GraphQL, REST APIs · Rust an advantage Cloud & DevOps: Docker, AWS (EC2, S3, Lambda and similar), Git, CI/CD, infrastructure as code (Terraform / OpenTofu) Testing: pytest, Playwright and similar automated testing frameworks Data: Columnar dataframes and Parquet, PostgreSQL, SQL Orchestration: Scheduled and event-driven pipelines for model training and batch inference Model Operations: Model registry and versioned artefacts, reproducible training runs, automated validation before release Monitoring: Pipeline and job observability, model performance and data drift tracking, product analytics AI Tooling: Claude Code, Cursor and similar AI-assisted development tools Analysis: Notebooks and Python visualisation libraries 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. Automated testing as a normal part of delivery, not an afterthought. Working fluently with AI coding tools such as Claude Code or Cursor as part of your day-to-day delivery. Clear written communication, and comfort working in a distributed team where technical decisions are documented and debated in writing. Comfortable working to a direction set by someone else, and confident asking for it when it is not clear. Preferred Qualifications Model performance monitoring and drift detection in production. Experience with an ML platform or model registry (MLflow, SageMaker, Metaflow, Kubeflow) — ours is in-house, so the concepts transfer more than the tool. Familiarity with gradient boosting and tabular machine learning; any exposure to causal inference or sequential decision making is a bonus. Product analytics tooling (PostHog, Amplitude, Mixpanel or similar), and using product usage data alongside model metrics to understand how recommendations are actually being used. Comfort reasoning about memory and throughput on large datasets. Multi-tenant SaaS architecture, including tenant isolation and per-customer model versioning. Dynamic pricing, revenue management, yield management or e-commerce pricing experience. Experience in the finance or fintech sector, particularly where automated decisions carry commercial consequences and need to be auditable. Rust, which we are progressively adopting for backend services. 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