Join a nationally significant organisation responsible for delivering critical weather, climate and environmental services that impact millions of Australians every day. This is an exciting opportunity for an experienced Machine Learning Engineer (MLOps) to work at the intersection of machine learning, cloud engineering and scientific data, helping transform advanced forecasting models into production-ready services.

About the Role

As a Machine Learning Engineer(MLOps), you will play a key role in designing, deploying and maintaining machine learning models that support forecasting, decision-support systems and customer-facing services.

Working within a multidisciplinary team of scientists, engineers and domain experts, you will develop scalable machine learning solutions using complex time-series, geospatial and scientific datasets. You'll be responsible for managing the full model lifecycle, from data ingestion and feature engineering through to deployment, monitoring and continuous improvement.

This role is ideal for someone who enjoys taking cutting-edge models beyond experimentation and into reliable, production-grade systems that deliver real-world outcomes.

Key Responsibilities

- Develop, deploy and optimise machine learning and statistical models supporting forecasting and decision-support services
- Work with large-scale time-series, geospatial and scientific datasets to deliver robust predictive solutions
- Design and implement end-to-end machine learning pipelines, including data ingestion, training, validation and deployment
- Operationalise machine learning models through APIs, automated workflows and backend services
- Monitor model performance through logging, alerting, drift detection,



retraining strategies and performance optimisation
- Deploy and manage ML workloads within cloud environments using modern MLOps practices
- Collaborate with scientists, engineers and business stakeholders to develop practical, data‑driven solutions
- Contribute to system architecture, tooling, automation and continuous improvement initiatives
- Document models, technical decisions and workflows to support knowledge sharing and maintainability
- Ensure solutions meet security, quality and operational requirements

Join a nationally significant organisation responsible for delivering critical weather, climate and environmental services that impact millions of Australians every day. This is an exciting opportunity for an experienced Machine Learning Engineer (MLOps) to work at the intersection of machine learning, cloud engineering and scientific data, helping transform advanced forecasting models into production-ready services.

About the Role

As a Machine Learning Engineer(MLOps), you will play a key role in designing, deploying and maintaining machine learning models that support forecasting, decision-support systems and customer-facing services.

Working within a multidisciplinary team of scientists, engineers and domain experts, you will develop scalable machine learning solutions using complex time-series, geospatial and scientific datasets.



You'll be responsible for managing the full model lifecycle, from data ingestion and feature engineering through to deployment, monitoring and continuous improvement.

This role is ideal for someone who enjoys taking innovative models beyond experimentation and into reliable, production-grade systems that deliver real-world outcomes.

Key Responsibilities

- Develop, deploy and optimise machine learning and statistical models supporting forecasting and decision-support services
- Work with large-scale time-series, geospatial and scientific datasets to deliver robust predictive solutions
- Design and implement end-to-end machine learning pipelines, including data ingestion, training, validation and deployment
- Operationalise machine learning models through APIs, automated workflows and backend services
- Monitor model performance through logging, alerting, drift detection, retraining strategies and performance optimisation
- Deploy and manage ML workloads within cloud environments using modern MLOps practices
- Collaborate with scientists, engineers and business stakeholders to develop practical, data‑driven solutions
- Contribute to system architecture, tooling, automation and continuous improvement initiatives
- Document models, technical decisions and workflows to support knowledge sharing and maintainability
- Ensure solutions meet security, quality and operational requirements

Skills & Experience Required

- Strong experience developing, training, validating and deploying machine learning models in production environments
- Proven expertise in Python and modern machine learning frameworks such as PyTo

📌 Machine Learning Engineer (Melbourne)
🏢 Calleo
📍 Melbourne