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Source: https://external-careers.jobs.unsw.edu.au/cw/en/job/538271/aimachine-learning-ops-engineer Parent: https://external-careers.jobs.unsw.edu.au/cw/en/listing/
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AI/Machine Learning Ops Engineer
Apply now Job no: 538271\ Work type: Full Time\ Location: Sydney, NSW\ Categories: Information Technology\
- Employment Type: Full-time, continuing role.
- Remuneration: Level 8 – from $127,351 plus 17% superannuation and leave loading
- Location: Kensington, NSW (Hybrid working model)
About UNSW
At UNSW, we pride ourselves on being a workplace where the best people come to do their best work. We aspire to be Australia’s global university, improving and transforming lives through excellence in research, outstanding education and a commitment to advancing a just society.
Why your role matters
The ML/AI Ops Engineer designs and maintains cloud-based ML/AI operations on UNSW’s enterprise data platform. This role operationalises applications by establishing robust MLOps processes and automated pipelines for data ingestion and model deployment. Key priorities include scalability, reliability, cost optimization, and cybersecurity.
The Engineer implements best practices for model lifecycle management, monitoring, and retraining while staying current with emerging AI technologies.
Reporting to the Senior Manager, Insight Analytics & Data, this position has no direct reports.
About the Role
The successful applicant will be required to undertake and have experience in the following areas of expertise:
- Collaborate with internal and external stakeholders to understand business needs and translate them into actionable ML/AI engineering plans and delivery.
- Design and implement large-scale, automated ML/AI pipelines within Azure and Databricks environments, spanning data acquisition through to model deployment.
- Develop scalable, cost-effective cloud infrastructure and orchestration processes to optimize data storage, security, and high-performance processing.
- Maintain comprehensive monitoring and logging solutions to ensure model accuracy, explainability, and real-time anomaly detection.
- Provide end-to-end MLOps support to meet institutional analytics needs while proactively managing technical risks and financials.
- Establish structured protocols and documentation to facilitate knowledge sharing and cross-functional collaboration across University divisions and faculties.
- Ensure total data integrity, privacy, and security by validating data quality and complying with all institutional regulations and policies.
About You
Tertiary qualifications in Computer Science or Software Engineering, or equivalent professional competency across data, cloud, and business intelligence engineering.
- Advanced proficiency in SQL, Python/PySpark, and database schema development, with experience managing structured and unstructured big data analysis via Unity Catalog.
- Extensive experience developing ML/AI pipelines in Azure and Databricks, utilizing tools like OpenAI Studio, Azure Functions, and Azure Machine Learning.
- Proven ability to implement MLOps and LLMOps principles, including prompt engineering for generative AI and the deployment of open-source Large Language Models.
- Skilled in using Azure DevOps for CI/CD and automated pipelines, with desirable experience in Kubernetes, Docker, and Infrastructure as Code tools like Terraform or ARM templates.
- Expertise in monitoring and optimizing ML/AI systems for reliability and cost-efficiency, incorporating Explainable AI and Responsible AI frameworks.
- Strong communication skills and familiarity with Agile methodologies, ensuring data security and privacy compliance within enterprise systems using tools like Jira and Git.
For further information on the role & responsibilities, please refer to the Position Description
How to Apply: Submit your CV & cover letter detailing your interest and suitability for the job (as per the skills & experience bullet points in the Position Description) before *Sunday 5th April by 11:55pm.*
Please note: Sponsorship is not available for this role, valid Australian working rights are required on application.
UNSW Benefits and Culture: People are at the core of everything we do. We recognise it is the contributions of our staff who make UNSW one of the best universities in Australia and the world. Our benefits include:
- Flexible Working Options (work from home, flexible hours etc)
- Additional 3 days leave during December festive period.
- Career development opportunities
- Up to 50% discount on UNSW courses
- Flexible 17% Superannuation contributions, additional leave-loading payments and salary sacrifice.
- Discounts and entitlements (retail, education, fitness passport)
UNSW is committed to equity diversity and inclusion. Applications from women, people of culturally and linguistically diverse backgrounds, those living with disabilities, members of the LGBTIQ+ community; and people of Aboriginal and Torres Strait Islander descent, are encouraged. UNSW provides workplace adjustments for people with disability, and access to flexible work options for eligible staff.
Pre-Employment Checks
Aligned with UNSW’s focus on cultivating a workplace defined by safety, ethical conduct, and strong integrity preferred candidates will be required to participate in a combination of pre-employment checks relevant to the role they have applied for.
These pre-employment checks may include a combination of some of the following checks:-
- National and International Criminal history checks
- Entitlement to work and ID checks
- Working With Children Checks
- Completion of a Gender-Based Violence Prevention Declaration
- Verification of relevant qualifications
- Verification of relevant professional membership
- Employment history and reference checks
- Financial responsibility assessments/checks.
- Medical Checks and Assessments
Compliance with the necessary combination of these checks is a condition of employment at UNSW.
Advertised: 06 Mar 2026 AUS Eastern Daylight Time\ Applications close: 05 Apr 2026 AUS Eastern Standard Time
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Search results
| Position | Location | Closes |
|---|---|---|
| AI/Machine Learning Ops Engineer | Sydney, NSW | 5 Apr 2026 |
| Design and manages automated, secure cloud pipelines to operationalize and scale UNSW’s enterprise ML/AI lifecycle. |
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| Position | Location | Closes |
|---|---|---|
| AI/Machine Learning Ops Engineer | Sydney, NSW | 5 Apr 2026 |
| Design and manages automated, secure cloud pipelines to operationalize and scale UNSW’s enterprise ML/AI lifecycle. |
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