Talent
Machine Learning Engineer's
TalentAustralia8 hours ago
ContractInformation Technology

The opportunity

Our client is a community focused Federal Government Agency. They are currently seeking a mid-level and a senior-level Machine Learning Engineer for a 6-month initial contract with the view for extension.


These contracts will commence as soon as possible with an office location in the Melbourne CBD, expecting 2 days a week onsite with the remainder from home. Open market hourly rates are on offer dependant on level of experience.


*Please note that Australian Citizenship is a requirement to be eligible to work for this Federal Government Agency*


Your responsibilities will include:

  • Design, implement, and maintain ML pipelines for model training, deployment, and monitoring.
  • Develop automation and CI/CD workflows for ML models using tools.
  • Ensure compliance with Australian Government security frameworks (ISM, PSPF) and agency policies.
  • Optimise ML infrastructure for performance, scalability, and cost-effectiveness.
  • Collaborate with data professionals to transition models from development to production.
  • Implement monitoring and alerting for model performance and data drift.
  • Provide technical advice and mentorship to team members and stakeholders.
  • Stay current with emerging ML Ops technologies and best practices.


About you

  • Demonstrated experience in ML Ops or related roles within complex environments.
  • Strong knowledge of ML lifecycle management and deployment strategies.
  • Proficiency in cloud platforms (AWS, Azure, GCP) and containerisation (Docker, Kubernetes).
  • Experience with ML Ops tools (Kubeflow, MLflow, Airflow) and CI/CD pipelines.
  • Solid understanding of data engineering, version control (Git), and automation frameworks.
  • Excellent problem-solving skills and ability to work under pressure.



APPLY

Submit your resume, or for further information please contact [email protected]


Desired Skills and Experience

Demonstrated experience in ML Ops or related roles within complex environments.
Strong knowledge of ML lifecycle management and deployment strategies.
Proficiency in cloud platforms (AWS, Azure, GCP) and containerisation (Docker, Kubernetes).
Experience with ML Ops tools (Kubeflow, MLflow, Airflow) and CI/CD pipelines.
Solid understanding of data engineering, version control (Git), and automation frameworks.
Excellent problem-solving skills and ability to work under pressure.

Key Skills

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