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 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.
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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n
Machine Learning
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n
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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n
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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Strong 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