Job Description

Hybrid Machine Learning / Data Engineer
n About the Role
n 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.
n 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.
n 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.
n Key Responsibilities
n Data Engineering

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

n

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

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

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- Take machine learning solutions beyond experimentation and into reliable production use.
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- Contribute to the architecture and design of production ML applications and services.
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- Implement model versioning, experiment tracking and release practices.
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- Work with CI/CD and automated testing for ML workloads.
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- Monitor system, data and model performance.
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- Diagnose production issues and improve reliability, scalability and performance.
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- Collaborate closely with MLOps, platform and engineering teams while maintaining ownership of the ML solution.
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You will:

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

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

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- Spark or Databricks
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- Airflow or similar orchestration tooling
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- MLflow, model registries, feature stores or experiment tracking
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- Docker and containerised applications
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- CI/CD for ML workloads
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- PyTorch or TensorFlow
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- NLP or document intelligence
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- LLM or Generative AI
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- Insurance, pricing, claims or another regulated industry
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📌 Senior Machine Learning / Data Engineer (New South Wales)
🏢 Correlate Resources
📍 New South Wales