AI/ML Infrastructure Engineer - MLOps

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

About the Role

Our client is looking for an experienced **AI/ML Infrastructure Engineer** to build and maintain the robust infrastructure powering their advanced **AI & Emerging Technologies** initiatives. This critical role, based in Queenstown, Eastern Cape , offers a flexible Hybrid work model, combining the benefits of in-office synergy with remote autonomy. You will be instrumental in designing, implementing, and scaling the systems that support our machine learning workflows, from data ingestion and model training to deployment and monitoring. This is an exceptional opportunity to work at the intersection of AI, cloud computing, and DevOps, ensuring the reliability and efficiency of our cutting‑edge ML operations (MLOps). You will collaborate closely with data scientists, ML engineers, and software developers to optimize our infrastructure and enable rapid iteration of AI models. Your expertise will be vital in building a foundation for scalable and sustainable AI development.

Key Responsibilities

• Design, build, and maintain scalable and reliable infrastructure for AI/ML workloads.
• Implement and manage MLOps pipelines, including CI/CD, automated testing, and model deployment.
• Optimize cloud resources (AWS, Azure, GCP) for cost‑efficiency and performance in ML contexts.
• Develop tools and automation scripts to streamline ML workflows.
• Monitor system performance, identify bottlenecks, and implement solutions.
• Ensure the security and compliance of ML infrastructure.
• Collaborate with data scientists and engineers to understand their infrastructure needs.
• Troubleshoot and resolve infrastructure‑related issues.

Requirements

• Bachelor's degree in Computer Science, Engineering, or a related field.
• Proven experience in infrastructure engineering, DevOps, or MLOps.
• Strong understanding of cloud platforms (AWS, Azure, or GCP) and their services.
• Experience with containerization technologies like Docker and orchestration tools like Kubernetes.
• Proficiency in scripting languages such as Python, Bash, or Go.
• Familiarity with machine learning concepts and workflows.
• Experience with CI/CD tools and practices.
• Excellent problem‑solving and troubleshooting skills.
• Ability to work collaboratively in a hybrid team environment.

Benefits

• Competitive salary package with performance bonuses.
• Comprehensive health, dental, and vision insurance.
• Generous paid time off and holidays.
• Opportunities for professional development and certifications.
• Access to state‑of‑the‑art tools and technologies.
• Flexible hybrid work environment.
• Collaborative and forward‑thinking company culture.