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 up-to-date 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 PyTorch, TensorFlow or Scikit-learn

📌 Machine Learning Engineer (Melbourne)
🏢 Calleo
📍 Melbourne