Principal AI Platform Engineer (ML focused leadership role)

Principal Machine Learning Platform Engineer

Sydney | Hybrid | Permanent

We're partnering with an established, technology-led organisation that is investing significantly in its machine learning and AI capabilities.

The business is looking for a Principal Machine Learning Platform Engineer to lead a small engineering team and shape the shared platform used across model research, large-scale inference and emerging Agentic AI initiatives.

This is an ML-first leadership role, rather than a traditional DevOps position. You'll remain approximately 70-80% hands-on, working closely with ML engineers and data scientists to improve how models are developed, trained, deployed, monitored and operated in production.

What you'll be working on

You'll lead a small ML platform team and work closely with three specialist AI groups:

• Model R&D, developing and improving production machine-learning models.
• Data and Inference, operating models across large and complex datasets.
• Agentic AI, exploring and productionising emerging AI and LLM use cases.

Your responsibilities will include:

• Owning the technical direction and roadmap for the shared ML platform.
• Leading and mentoring a team of three ML engineers.
• Building production-grade platform components and services in Python.
• Improving model-training, evaluation, deployment and inference workflows.
• Creating a consistent developer experience for ML engineers and data scientists.
• Supporting reproducible experimentation, model versioning and artifact management.
• Improving model monitoring, observability and production reliability.
• Helping teams optimise the cost and performance of CPU and GPU workloads.
• Working with dedicated cloud and infrastructure specialists across AWS and Kubernetes.
• Establishing engineering standards across testing, security, reliability and production operations.
• Evaluating tools such as Kubeflow, SageMaker, Argo Workflows, Flyte, Ray and KubeRay where appropriate.

What we're looking for

You'll bring substantial hands-on experience building and operating production ML systems, along with the technical leadership capability to influence multiple engineering teams.

Ideally, you will have:

• Strong commercial experience across machine learning and software engineering.
• Advanced Python engineering skills.
• Experience with frameworks such as PyTorch, TensorFlow or scikit-learn.
• A strong understanding of model training, evaluation, inference and productionisation.
• Experience designing or improving ML pipelines and shared platform capabilities.
• Exposure to workflow orchestration through Kubeflow Pipelines, Airflow, Argo Workflows, Flyte or similar technologies.
• Experience running ML workloads in AWS, GCP or another major cloud environment.
• Practical familiarity with containers and Kubernetes, although deep cluster administration is not essential.
• Experience leading, mentoring or providing technical direction to ML engineers.
• An understanding of model monitoring, observability, reproducibility and production reliability.
• The ability to work effectively across ML, data science, product and infrastructure teams.

Experience in any of the following would be particularly valuable:

• Computer vision or other computationally intensive ML workloads.
• Distributed training or GPU-based workloads.
• High-volume batch or real-time inference.
• Ray, KubeRay, SageMaker, Vertex AI or Kubeflow.
• Feature stores, model registries and experiment-tracking platforms.
• LLMOps, RAG, Agentic AI or AI evaluation.
• Building an internal ML platform used by multiple engineering teams.
• Large-scale data processing or distributed systems.

Why consider this opportunity?

• Genuine technical ownership: Shape the platform architecture and roadmap rather than simply delivering a predetermined backlog.
• Remain hands-on: The role is expected to stay approximately 70-80% technical.
• Complex ML challenges: Work across model development, large-scale inference and emerging Agentic AI use cases.
• Visible organisational impact: Build the platform used by several specialist AI teams.
• Small, capable team: Lead one Senior and two Mid-level ML Engineers.
• Dedicated infrastructure support: Work alongside specialists who manage the deeper AWS and Kubernetes environment.
• Hybrid working: Combine collaborative office time with flexibility for focused technical work.

This opportunity would suit a Principal ML Engineer, Staff ML Engineer, Lead ML Engineer or senior ML Platform Engineer who wants broader technical ownership without moving into a management-only role.