MLOps & AI Infrastructure Engineer — Hybrid Role

Placements24; Queenstown; Hybrid; NZD 110,000 - 150,000; Full time

Benefits offered by this job

Health insurance

Bonuses

Hybrid work arrangement

Professional development

Job summary

Placements24 is seeking an AI/ML Infrastructure Engineer in Queenstown, NZ, to design, build, and maintain scalable infra for AI and emerging technologies. You will implement MLOps pipelines, optimize cloud resources, and collaborate with data scientists and software engineers to enable rapid model development and deployment in a hybrid setting.

The role focuses on CI/CD, security, cost control, and reliable ML workflows with Docker, Kubernetes, and cloud services.

Qualifications

• Bachelor's degree in Computer Science, Engineering, or related field.
• Proven experience in infrastructure engineering, DevOps, or MLOps.
• Strong understanding of cloud platforms (AWS, Azure, or GCP) and their services.
• Experience with Docker and Kubernetes.
• Proficiency in scripting languages such as Python, Bash, or Go.
• CI/CD practices and tooling.

Responsibilities

• Design, build, and maintain scalable infra for AI/ML workloads.
• Implement and manage MLOps pipelines, including CI/CD and deployment.
• Optimize cloud resources for cost-efficiency and performance.
• Develop tools and automation for ML workflows.
• Monitor performance, troubleshoot issues, and ensure security.
• Collaborate with data scientists and engineers.

Skills

Cloud platforms

MLOps

CI/CD

Python

Go

Bash

Troubleshooting

Collaboration

Education

Bachelor's degree in CS/Engineering

Tools

Docker

Kubernetes

Job description

Placements24 is seeking an AI/ML Infrastructure Engineer in Queenstown, NZ, to design, build, and maintain scalable infra for AI and emerging technologies. You will implement MLOps pipelines, optimize cloud resources, and collaborate with data scientists and software engineers to enable rapid model development and deployment in a hybrid setting.

The role focuses on CI/CD, security, cost control, and reliable ML workflows with Docker, Kubernetes, and cloud services.