Hybrid Machine Learning / Data Engineer
About the Role
An exciting opportunity is available for aHybrid Machine Learning / Data Engineer to join a team developing production machine learning solutions using complex business, operational and document data.
This is a genuinely hybrid engineering role spanning Data Engineering and Machine Learning. You will work across the complete lifecycle — from ingesting and transforming raw data through feature engineering, model development, deployment, monitoring and ongoing improvement.
This is not a traditional Data Scientist position or a pure Data Engineering role. You will be expected to operate comfortably across both disciplines while taking strong technical ownership of production ML solutions.
Key Responsibilities
Data Engineering

- Build and maintain scalable data ingestion and transformation pipelines.
- Work with structured, semi-structured and unstructured data.
- Transform raw or inaccessible information into reliable, model-ready datasets.
- Design pipelines with appropriate data quality and validation controls.
- Work with large and complex datasets using Python, SQL and modern data engineering frameworks.
- Ensure pipelines are maintainable, observable and suitable for production environments.
- Translate business problems into appropriate machine learning solutions.
- Build, train, validate and evaluate ML models.
- Design features based on business requirements and available data.
- Establish appropriate baselines and compare alternative modelling approaches.
- Define meaningful evaluation metrics and validation strategies.
- Identify and manage issues including leakage, overfitting, bias and model degradation.
- Support models through deployment, monitoring and retraining.

Hybrid Machine Learning / Data Engineer
About the Role
An exciting prospect is available for aHybrid Machine Learning / Data Engineer to join a team developing production machine learning solutions using complex business, operational and document data.
This is a genuinely hybrid engineering role spanning Data Engineering and Machine Learning. You will work across the complete lifecycle — from ingesting and transforming raw data through feature engineering,



model development, deployment, monitoring and ongoing improvement.
This is not a traditional Data Scientist position or a pure Data Engineering role. You will be expected to operate comfortably across both disciplines while taking strong technical ownership of production ML solutions.
Key Responsibilities
Data Engineering

- Build and maintain scalable data ingestion and transformation pipelines.
- Work with structured, semi-structured and unstructured data.
- Transform raw or inaccessible information into reliable, model-ready datasets.
- Design pipelines with appropriate data quality and validation controls.
- Develop reusable feature engineering workflows.
- Work with large and complex datasets using Python, SQL and modern data engineering frameworks.
- Ensure pipelines are maintainable, observable and suitable for production environments.

Machine Learning
- Translate business problems into appropriate machine learning solutions.
- Build, train, validate and evaluate ML models.
- Design features based on business requirements and available data.
- Establish appropriate baselines and compare alternative modelling approaches.
- Define meaningful evaluation metrics and validation strategies.
- Identify and manage issues including leakage, overfitting, bias and model degradation.
- Support models through deployment, monitoring and retraining.

Production ML
- Take machine learning solutions beyond experimentation and into reliable production use.
- Contribute to the architecture and design of production ML applications and services.
- Implement model versioning, experiment tracking and release practices.
- Work with CI/CD and automated testing for ML workloads.
- Monitor system, data and model performance.
- Diagnose production issues and improve reliability, scalability and performance.




- Collaborate closely with MLOps, platform and engineering teams while maintaining ownership of the ML solution.

You will:
- Independently work through ambiguous and complex technical problems.
- Make and defend architecture, pipeline and modelling decisions.
- Identify risks and bottlenecks across data and ML systems.
- Review and challenge pipeline, feature engineering and modelling approaches.
- Provide technical guidance and mentoring to other engineers.
- Help establish practical engineering and modelling standards.
- Clearly communicate technical decisions and trade-offs to stakeholders.
- Provide technical depth across multiple ML initiatives where required.

About You You will bring:
- Strong commercial experience across Machine Learning and Data Engineering.
- Strong hands-on Python and SQL skills.
- Demonstrated experience building data pipelines and production ML solutions.
- Experience across data ingestion, transformation and feature engineering.
- Experience taking ML models from development through to production.
- Strong understanding of model training, validation and evaluation.
- Experience with model monitoring and lifecycle management.
- Solid software engineering fundamentals.
- Experience working in cloud-based data and/or ML environments.
- Experience working with large, complex or unstructured datasets.
- Strong understanding of production reliability, scalability and maintainability.
- The ability to explain technical decisions and trade-offs clearly.

Desirable Experience Exposure to any of the following would be beneficial:
- Spark or Databricks
- Airflow or similar orchestration tooling
- MLflow, model registries, feature stores or experiment tracking
- Docker and containerised applications
- CI/CD for ML workloads
- PyTorch or TensorFlow
- NLP or document intelligence
- LLM or Generative AI
- Insurance, pricing, claims or another regulated industry

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📌 Senior Machine Learning / Data Engineer (New South Wales)
🏢 Correlate Resources
📍 New South Wales