Director of Infrastructure, MLOps

Director of Infrastructure | Sydney, Hybrid/Remote | MLOps, Infrastructure, AI, Computer Vision, Kubernetes

Are you ready to lead a cutting-edge engineering team for a reputable & impactful, tech-first organization operating at the intersection of artificial intelligence and cloud-native engineering?

With proprietary technology, AI-driven analysis pipelines, and robust SaaS offerings, they seek a deeply experienced technical and people leader to guide their AI & Infrastructure Engineering squad, managing large-scale, cloud-based infrastructures.

This team connects cloud operations with applied Computer Vision + AI efforts, ensuring workflows are reliable, observable, and fast—scaling to thousands of compute nodes or integrating the latest generative AI technologies.

Director of MLOps Responsibilities

1. Spearhead design and optimization of highly parallelized, distributed processing systems for large-scale AI workloads — handling petabytes of data and dynamic resource provisioning.
2. Lead deployment and monitoring of real-time machine learning models at scale using modern orchestration platforms.
3. Build developer-centric tooling and frameworks to help ML Engineers and Data Scientists rapidly prototype, train, and deploy models.
4. Drive adoption and integration of emerging technologies, including large language models, into core platform functionality.
5. Collaborate across DevOps, Cloud Engineering, and AI teams to ensure seamless alignment of priorities, infrastructure reliability, and delivery timelines.
6. Manage and mentor a global team of engineers and ML infrastructure specialists across multiple regions.

Director of MLOps Requirements

• Cloud & Orchestration: AWS, EKS, Karpenter, Terraform, ArgoCD
• AI Infrastructure: Ray, Kubeflow, Weights & Biases (WandB), Kafka (via Confluent)
• Workflows & Pipelines: DAG-based batch systems, streaming pipelines, real-time inference architectures
• Languages: Go, Python, Bash, and other backend-focused languages
• Experience as a backend infrastructure or platform engineer with scaling complex systems in cloud-native environments.
• Strong technical leadership skills, empowering AI teams by solving engineering challenges.
• Experience with large-scale distributed computing, observability, and MLOps principles.
• Ability to operate reliably in high-stakes environments demanding speed and precision.
• Passion for bridging data science and cloud engineering.

If interested, please apply with your most up-to-date CV and reach out.