MLOps Engineer — Python/Kubernetes | Govt Contract, Onsite 6wks

Infinity Pro; Sydney; Presencial; AUD 110.000 - 165.000; Jornada completa

Descripción de la vacante

Infinity Pro is seeking an experienced MLOps Engineer / Machine Learning Engineer for a senior contract with a Federal Government agency.

You will help turn machine-learning solutions into reliable production services, combining data pipelines, model integration, automated testing, deployment, monitoring and retraining. Working with data scientists, engineers and business stakeholders, you will also contribute to MLOps strategy, engineering standards and secure, maintainable delivery.

Formación

• At least three years’ AI/ML engineering experience with deployment and monitoring of ML systems.
• Strong data-engineering capability including Python, APIs, SQL and Spark/PySpark, with batch and streaming pipelines.
• Practical experience with Git, Linux, containerisation, Kubernetes and automated CI/CD, plus MLflow/Kubeflow or equivalent platforms.

Responsabilidades

• Build and operate batch and streaming data pipelines for ML workloads.
• Implement models and integrate with data stores and computing environments.
• Develop automated pipelines for testing, deployment and monitoring of ML models.
• Lead testing, release planning and risk assessments for production ML workloads.
• Contribute to MLOps strategy, security and governance in collaboration with stakeholders.

Conocimientos

AI/ML engineering

Data engineering

CI/CD

Git

Linux

Containerisation

Kubernetes

MLflow/Kubeflow

Educación

Advanced degree in CS/Statistics/Math

Herramientas

Python

Spark/PySpark

Kubernetes

MLflow

Kubeflow

Git

Linux

OpenShift

Descripción del empleo

Infinity Pro is seeking an experienced MLOps Engineer / Machine Learning Engineer for a senior contract with a Federal Government agency.

You will help turn machine-learning solutions into reliable production services, combining data pipelines, model integration, automated testing, deployment, monitoring and retraining. Working with data scientists, engineers and business stakeholders, you will also contribute to MLOps strategy, engineering standards and secure, maintainable delivery.