MLOps Engineer
7-Eleven Australia
MLOps Engineer
7-Eleven Australia is seeking an MLOps Engineer to help the Data Science team deploy, operate and maintain machine learning and AI solutions in production. The role focuses on operationalising a strategic ML engine hosted in Databricks and supporting a growing portfolio of predictive modelling, optimisation and AI use cases. Requires strong Python, SQL and PySpark skills, plus experience with Azure Databricks, MLflow, Delta Lake, Unity Catalog and CI/CD practices.
MLOps Engineer
7-Eleven Australia is seeking an MLOps Engineer to help the Data Science team deploy, operate and maintain machine learning and AI solutions in production. The role focuses on operationalising a strategic ML engine hosted in Databricks and supporting a growing portfolio of predictive modelling, optimisation and AI use cases. Requires strong Python, SQL and PySpark skills, plus experience with Azure Databricks, MLflow, Delta Lake, Unity Catalog and CI/CD practices.
Salary
Core Qualifications
Technical (Must-have)
Soft Skills
Preferred Qualifications
Technical (Nice-to-have)
Key Responsibilities
- Partner with Data Science to take models from experimentation through validation, deployment and ongoing production management
- Implement and maintain production workflows for data ingestion and processing, model execution, retraining, testing and deployment
- Build and maintain CI/CD pipelines and controlled release processes for machine learning and AI workloads
- Build and maintain robust data and feature pipelines required by machine learning and AI solutions
- Diagnose production issues and work with Data Science and Engineers to resolve model, data and platform problems
- Implement appropriate access controls, security and governance within Databricks, including access and action permissions for AI agents and automated systems in line with enterprise standards
- Support operationalising of Generative AI solutions, including LLM applications, RAG and emerging AI use cases
- Contribute to reusable templates, tooling and MLOps practices that make it easier to deploy new models consistently
- Contribute to establishing best practice, configure and set up state of the art tooling to meet production standards
- Support performance optimisation and efficient use of Databricks and cloud infrastructure