Machine Learning Engineer

We are seeking an ML Engineer to build, deploy, and operationalize machine learning models as part of a data platform modernization program for an Australian payments company. This role focuses on end-to-end ML pipeline development—from feature engineering and model training to deployment, monitoring, and inference—using AWS SageMaker and related services. The ideal candidate can translate Data Scientist prototypes into production-grade ML systems with robust monitoring and automation.

Required Skills :

• AWS SageMaker – Deep hands-on experience with SageMaker for training, hosting, pipelines, and model registry.
• Python – Expert-level Python for ML development (scikit-learn, XGBoost, LightGBM, PyTorch, or TensorFlow).
• Feature Engineering – Proven experience building feature pipelines and managing feature stores.
• Model Deployment – Hands-on experience deploying ML models to production endpoints (real-time and batch).
• Model Monitoring – Experience implementing drift detection, performance dashboards, and automated alerting for deployed models.
• MLOps – Understanding of MLOps principles: CI/CD for ML, experiment tracking, model versioning, and automated retraining.
• SQL – Strong SQL skills for feature extraction and data validation from Snowflake or similar warehouses.