MLOps Engineer: Scale & Stabilize AI in Production

Placements24; Canterbury; On-site; NZD 100,000 - 150,000; Full time

Benefits offered by this job

Competitive salary

Comprehensive benefits package

Job summary

Placements24 is seeking a skilled MLOps Engineer to improve the reliability and scalability of AI systems in Queenstown. You will bridge model development and deployment, automating the entire ML lifecycle to ensure robust, efficient production solutions.

Join a growing tech team focused on best practices in AI deployment and operations, collaborating with data scientists and engineers to optimize infrastructure, monitoring, and cost efficiency in a dynamic environment.

Qualifications

• Bachelor's degree in CS, Engineering, or a related field.
• 3+ years of experience in software engineering, DevOps, or MLOps.
• Proficiency in Python and cloud platforms (AWS, Azure, GCP).
• Experience with containerization (Docker, Kubernetes).
• Familiarity with ML frameworks (TensorFlow, PyTorch, scikit-learn).
• Understanding of ML lifecycle and deployment challenges.

Responsibilities

• Design, build, and maintain CI/CD pipelines for ML models.
• Automate deployment, monitoring, and management of ML models in production.
• Implement infrastructure for data/versioning and experiment tracking.
• Monitor ML performance in production and resolve issues.
• Collaborate with data scientists and engineers for lifecycle management.
• Optimize ML infrastructure for cost-efficiency and scalability.

Skills

Python scripting

CI/CD pipelines

Cloud platforms

Docker & Kubernetes

ML frameworks

MLOps tools

Team collaboration

Education

Bachelor's degree in Computer Science/Engineering

Tools

Docker

Kubernetes

TensorFlow

PyTorch

Job description

Placements24 is seeking a skilled MLOps Engineer to improve the reliability and scalability of AI systems in Queenstown. You will bridge model development and deployment, automating the entire ML lifecycle to ensure robust, efficient production solutions.

Join a growing tech team focused on best practices in AI deployment and operations, collaborating with data scientists and engineers to optimize infrastructure, monitoring, and cost efficiency in a dynamic environment.