Senior Hybrid Machine Learning / Data Engineer
We’re seeking a hands-on Senior Engineer to work across data engineering and machine learning. You’ll help turn raw policy, claims, operational and document data into reliable datasets, useful features and production-ready ML solutions.
The role covers the full lifecycle: data ingestion and transformation, feature engineering, model development and validation, productionisation, monitoring and ongoing improvement. You’ll contribute across two related workstreams, with a focus on building practical, reliable solutions from complex data.

Key responsibilities
Data engineering

- Build and maintain scalable data ingestion and transformation pipelines.
- Work with structured, semi-structured and unstructured data.
- Create usable, model-ready datasets from information that may be difficult to access or not yet available in a suitable format.
- Build in data quality and validation controls.
- Develop reusable feature engineering workflows using Python, SQL and modern data engineering frameworks.
- Make pipelines maintainable, observable and suitable for production.

Machine learning

- Translate business problems into appropriate machine learning solutions.
- Build, train, validate and evaluate models.
- Develop features, baselines, evaluation metrics and validation strategies suited to each problem.
- Address risks such as data leakage, overfitting, bias and model degradation.
- Support model deployment, monitoring and retraining.

Production ML

- Take ML solutions from experimentation into reliable production use.
- Contribute to the design of production ML applications and services.




- Support model versioning, experiment tracking, release practices, CI/CD and automated testing.
- Monitor system, data and model performance, and diagnose production issues.
- Work with MLOps, platform and engineering teams while maintaining ownership of the ML solution.

About you
- Robust commercial experience across both machine learning and data engineering.
- Hands-on Python and SQL skills.
- Experience building data pipelines and production ML solutions.
- Experience with data ingestion, transformation and feature engineering.
- Experience taking models from development into production.
- Strong understanding of model training, validation, evaluation and monitoring.
- Sound software engineering fundamentals and experience with cloud-based data or ML.
- Experience working with large, complex or unstructured datasets.
- Understanding of production reliability, scalability and maintainability.
- Clear communication skills, including the ability to explain technical decisions and trade-offs.

At senior level, you’ll work autonomously through ambiguous technical problems, make and explain architecture and modelling decisions, identify risks across data and ML systems, and provide technical guidance to a mid-level engineer.
Desirable experience

- Spark or Databricks.
- Airflow or similar workflow orchestration.
- MLflow, model registries, feature stores or experiment tracking.
- Docker or containerised applications.
- CI/CD for ML workloads.
- PyTorch or TensorFlow.
- NLP or document intelligence.
- LLM or generative AI exposure.
- Insurance, pricing, claims or other regulated-industry experience.

📌 Senior Hybrid Machine Learning / Data Engineer (Sydney)
🏢 Correlate Resources
📍 Sydney