Senior MLOps Engineer

Talent; Sydney; Hybrid; AUD 170,000 - 210,000; Full time

Job summary

Talent is seeking a Senior MLOps Engineer to support a major Federal Government organisation. You will design and operate automated MLOps pipelines, build data workflows and containerised ML environments with Kubernetes and OpenShift AI.

Collaborate with data scientists and engineers to deploy secure, scalable ML solutions. Responsibilities include testing, release management, model monitoring and responsible AI practices.

Qualifications

• Hands-on AI/ML engineering experience delivering production ML solutions.
• Strong programming and data engineering with Python, SQL and Spark/PySpark.
• Experience with MLOps frameworks like MLflow or Kubeflow for tracking, versioning and monitoring.
• Kubernetes, containerisation and Linux experience, ideally Red Hat OpenShift AI.
• CI/CD, Git, infrastructure automation and secure release practices.

Responsibilities

• Design, develop and maintain automated MLOps pipelines for model development, testing, deployment, monitoring and retraining.
• Build batch and real-time data pipelines to support enterprise ML workloads.
• Manage containerised ML environments using Kubernetes and OpenShift AI.
• Collaborate with data scientists and engineers to transition models into production.
• Establish automated testing, release management and secure deployment processes.
• Implement model monitoring, experiment tracking and version control for reliability.

Job description

• Contract: Initial contract of up to 12 months, with extensions.
• Location: Brisbane, Melbourne or Sydney. Initial 6 weeks onsite, with hybrid working arrangements potentially available thereafter.
• Security Clearance: Must have the ability to obtain a Baseline security clearance.
• Key Technologies: Python, SQL, Spark/PySpark, Kubernetes, Red Hat OpenShift AI, CI/CD, MLflow/Kubeflow and enterprise data platforms.

We are currently seeking an experienced Senior MLOps Engineer to support a major Federal Government organisation in developing and operationalising enterprise-scale artificial intelligence and machine learning capabilities. This is an opportunity to work on complex, high-impact technology initiatives, building the platforms, automation and processes that enable machine learning models to be securely deployed, monitored and maintained in production environments.

Your duties will include:

• Design, develop and maintain automated MLOps pipelines supporting machine learning model development, testing, deployment, monitoring and retraining.
• Build and optimise batch and real-time data pipelines to support enterprise machine learning and analytics workloads.
• Implement and manage containerised machine learning environments using Kubernetes, preferably Red Hat OpenShift AI.
• Collaborate with data scientists, software engineers and business stakeholders to transition machine learning models into reliable production solutions.
• Establish automated testing, release management and deployment processes, ensuring appropriate security, governance and rollback capabilities.
• Implement model monitoring, experiment tracking and version control to improve the reliability, performance and maintainability of machine learning solutions.
• Support responsible AI practices, including privacy, security, compliance and ongoing improvements to MLOps engineering standards.

Skills and Experience we are looking for:

Demonstrated experience in AI/ML engineering, including hands-on experience deploying and supporting machine learning solutions in production environments.

Strong programming and data engineering skills using Python, SQL and Spark/PySpark, including experience developing batch and streaming data pipelines.

• Practical experience with MLOps frameworks such as MLflow, Kubeflow or equivalent technologies for experiment tracking, model versioning and monitoring.
• Experience with Kubernetes, containerisation and Linux, ideally including Red Hat OpenShift AI and managing compute resources for machine learning workloads.
• Strong understanding of CI/CD, Git, infrastructure automation and DevSecOps, including automated testing, deployment orchestration and secure release management.
• Experience working with enterprise data platforms, relational databases, distributed storage and data lake technologies such as Hadoop, Hive, Impala and Kafka. Experience with Cloudera Data Platform would be highly regarded.
• Demonstrated ability to lead technical delivery, troubleshoot complex production issues and communicate effectively with multidisciplinary teams. Relevant tertiary qualifications in computer science, mathematics, statistics or a related discipline will be highly regarded.

Experience working with on-premises machine learning infrastructure is particularly relevant, with potential opportunities to support future transitions to AWS or Microsoft Azure cloud environments.