MLOps Engineer

We offer a comprehensive pay and benefits package designed to stay competitive in the market, support your wellbeing, and recognise your contribution to our success. Our approach is underpinned by a pay-for-performance belief in rewarding individual impact. Your specific compensation package will be determined by factors such as your skills, experience, qualifications, and location.

Depending on your role and location, you may be eligible for annual bonuses and incentives, health and wellbeing benefits, paid time off, retirement plans, insurance coverage, and other local or statutory benefits.

Specific details about compensation and benefits for this position will be discussed during the recruitment process.

At Aristocrat, we are advancing gaming technology with modern, powerful solutions. Our mission is to bring happiness to life through play. We value collaboration, creativity, and a strong passion for excellence. These roles primarily support the Machine Learning area of our Product Madness business line - a tech company specialised in social casino mobile games. We are looking for an MLOps Engineer to join our MLOps team and build and own the platform that takes our machine learning and AI systems reliably to production. You will focus on serving models as performant, real-time services, on the CI/CD and infrastructure around them, and on the tooling that lets our ML Engineers and Data Scientists ship, track and monitor models with confidence. You will make a real difference in our products and players' experiences.

• Deploy machine learning models as reliable, real-time APIs, with a strong focus on latency, throughput, caching, and scaling.
• Design and own the serving and deployment infrastructure: containers, orchestration (Kubernetes), and serving frameworks (e.g., KServe, Triton, BentoML) where appropriate.
• Build and maintain CI/CD pipelines for models, and orchestrate training and retraining workflows (e.g., Airflow).
• Own the platform side of experiment tracking and model registry: versioning, lineage, promotion, and governance, so model releases are controlled and reproducible.
• Build the monitoring and observability infrastructure that ML Engineers use to track model and system health (metrics, logging, tracing, alerting).
• Ensure reproducibility across environments and pipelines, and harden systems for reliability (retries, timeouts, graceful degradation, rollback).
• Partner with ML Engineers and Data Scientists to smooth the path from prototype to production, and with engineering on deeper system-design questions.

Travel Expectations- Minimal travel required (up to 5%)

Hybrid role with 3 days at the office weekly.