Senior Machine Learning Ops Engineers
Senior Machine Learning Ops Engineers
Employer Active
Job Alert
You will be updated with latest job alerts via email
Share
Job Alert
You will be updated with latest job alerts via email
Job Description
Job Description
Senior Machine Learning Ops Engineers
- Initial 12-month contract starting November 2025
- Australian Citizens with an active Positive Vetting Government clearance
- Located on-site in Canberra ACT
Machine Learning (ML) Engineers are involved in setting the overall ML Ops strategy for the organisation as well as the delivery of complex projects. They will work closely with cross-functional teams including data scientists engineers and business stakeholders to ensure that ML initiatives are aligned with industry standards and business goals.
This position is well suited to a candidate with strong software engineering or data engineering expertise who has had exposure to contemporary Machine Learning practices and technologies.
The ideal candidate has experience with all parts of the MLOps lifecycle including the registration deployment and monitoring of operation capabilities. It is expected that you will deliver key platforms and integrations to deliver self-service abilities to Data Scientists to achieve continuous integration continuous deployment continuous training and continuous monitoring.
Key Duties and Responsibilities:
- Design develop and maintain production MLOps platforms specific to ASD.
- Deploy monitor and troubleshoot ML models in production environments.
- Design and implement MLOps pipelines for deploying ML models to production.
- Review and optimise production ML code.
- Work with open-source technology and modern computing infrastructure.
- Work with other engineers to ensure successful integration into enterprise software.
- Work with data scientists to ensure that ML models are well tested and reliable.
Essential Criteria:
- Designs implements and maintains complex data engineering solutions to acquire and prepare data.
- Creates and maintains data pipelines to connect data across stores applications and organisations.
- Builds in compliance with data governance and security standards. Supports the development of continuous integration and deployment practices.
- Monitors and optimises pipeline performance and scalability. Conducts complex data quality checking and remediation. Leads data migration and data conversion activities.
- Leads the development and implementation of machine learning solutions for complex high-impact business problems. Architects end-to-end machine learning pipelines and systems incorporating MLOps practices.
- Evaluates and selects tools frameworks and infrastructure for machine learning projects. Establishes practices and standards for machine learning development and operations.
- Provides expert advice and guidance on machine learning techniques and applications.
- Collaborates with stakeholders to align machine learning initiatives with organisational goals.
- Plans and schedules releases in line with business requirements and objectives. Coordinates release activities across multiple teams and stakeholders.
- Manages the release lifecycle ensuring timely and quality deliverables. Ensures releases meet defined quality security and compliance standards.
- Communicates release plans progress and outcomes to stakeholders. Conducts post-release reviews and identifies areas for improvement.
- Provides technical expertise to enable the configuration of system components and equipment for systems testing. Collaborates with technical teams to develop and agree system integration plans and report on progress.
- Defines complex/new integration builds. Ensures integration test environments are correctly configured.
- Designs performs and reports results of tests of the integration build. Identifies and documents system integration components for recording in the configuration management system.
- Recommends and implements improvements to processes and tools.
Desirable: (experience in one or more of the following areas)
- Software development with Python.
- Experience with MLOps tooling such as MLFlow Ray KubeFlow Kserve or an enterprise ML platform.
- Building DevOps pipelines with GitLab CI/CD or equivalent.
- Kubernetes Docker.
- Data engineering and data wrangling.
- Optimisation of model for production inference.
Role particulars:
Submission due: Monday 21st July 2025
Duration: 03/11/2025 02/11/2026
Extension/s: 2 x 12 monthoption to extend
Security clearance: Active Positive Vetting clearance is essential
Location: Canberra A.C.T
Working arrangements:On-site
Required Experience:
Senior IC
Employment Type
Contract
