The Role
- Productionise machine learning models built by data scientists, ensuring robustness, scalability and performance
- Design feature pipelines, model training workflows and inference services
- Build and maintain CI/CD pipelines for ML models, covering training, testing, deployment and monitoring
- Implement model versioning, experiment tracking and reproducibility standards
- Set up monitoring for model performance, drift, data quality and operational health
- Operate ML workloads on cloud platforms, with containerisation and orchestration where it makes sense
- Embed responsible AI practices, including explainability and bias monitoring, in a regulated environment
Must-haves:
- Over 3 years in ML engineering, MLOps, or software/data engineering with ML in production
- Strong Python skills (familiarity across scikit-learn, TensorFlow, PyTorch or XGBoost is preferred)
- Experience building and operating ML pipelines in a production environment
- Solid software engineering fundamentals: testing, CI/CD, version control
- Experience with cloud platforms and managed ML services
- Background in financial services or insurance
- Familiarity with MLflow, Kubeflow, Airflow, SageMaker, Vertex AI or Azure ML
- Exposure to model explainability techniques such as SHAP or LIME
You'll sit right at the intersection of data science, engineering and cloud, with a direct hand in how AI gets deployed and trusted across the business. It's a role for someone pragmatic and delivery-focused who wants ownership of the platform, not just the pipeline.
How to Apply
Apply now, or reach out to Bruce Batters at Momentum Consulting for a confidential chat: bruce.batters@momentum.co.nz
