Job Details

Location

Sydney, NSW (Hybrid)

Salary
On request

Job Type

Permanent

Correlate Resources: Your trusted Data, AI and Technology Recruitment Partner. Apply now for exclusive opportunities, market insights and ongoing career coaching.

• HYBRID SENIOR ENGINEER — Data engineering and machine learning in one production seat

• FROM PIPELINE TO PRODUCTION — Ingest, features, models, monitoring and retraining

• TECHNICAL OWNERSHIP — Architecture decisions, mentoring and standards across ML initiatives

An exciting opportunity is available for a Hybrid 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 be expected to operate with a high level of autonomy and technical judgement.

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.

• Strong 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

Spark or Databricks; Airflow or similar orchestration; MLflow, model registries, feature stores or experiment tracking; Docker; CI/CD for ML workloads; PyTorch or TensorFlow; NLP or document intelligence; LLM or Generative AI; insurance, pricing, claims or another regulated industry.

To apply for this position please click the Apply Now button below or send your resume and a cover note to ben@correlateresources.com