ML Platform / DevOps Engineer

present of AI; Australia; Remoto; AUD 120.000 - 180.000; Tempo pieno

Descrizione del lavoro

Metacognition AI seeks a platform-focused engineer to build and run cloud infrastructure that lets researchers and engineers try ideas and ship software safely. The role emphasizes hands-on DevOps, ML platform work, and secure, self-service tooling.

You’ll work with GitHub Actions pipelines, Terraform or Pulumi, containers, Kubernetes, and cloud providers (AWS or GCP) to scale compute and data, while maintaining guardrails and access controls.

Competenze

• Hands-on DevOps and ML platform experience.
• Experience with GitHub Actions pipelines, Terraform or Pulumi, containers, Kubernetes, and AWS or GCP.
• Strong grasp of access controls and security, and the ability to build reusable self-service tooling.

Mansioni

• Build and run the cloud setup that enables researchers and engineers to try ideas and ship software safely.
• Maintain scalable pipelines and platform tooling with guardrails.
• Collaborate with researchers to gate access and ensure secure, reproducible deployments.
• Advise on architecture and best practices for secure, scalable AI workloads.

Conoscenze

GitHub Actions

Terraform

Pulumi

Kubernetes

Containers

AWS

GCP

DevOps

ML platform

Access controls

Strumenti

GitHub Actions pipelines

Terraform

Pulumi

Kubernetes

Docker

Descrizione del lavoro

The role centres on building and running the cloud setup that lets researchers and engineers try ideas and ship software safely. A strong candidate has hands-on DevOps and ML platform experience, especially with GitHub Actions pipelines, Terraform or Pulumi, containers, Kubernetes, and AWS or GCP. They also bring a sound grasp of access controls and security, plus the software skills to build reusable self-service tooling. The posting says scope will be matched to the person, so seniority is not fixed.

How it fits the AI era

AI makes the training, serving and testing workloads heavier and changes quickly, so the platform has to scale compute and data on demand while keeping guardrails in place. Agentic tools can speed up routine pipeline and knowledge-sharing work, but deciding on architecture, access rules and safe defaults stays with people. Trust in the platform comes from careful judgment about what teams and agents are allowed to do.

How it builds AI experience

This job offers close contact with research teams working on a system that controls what AI agents can do, which is a useful place to learn how AI workloads are run and governed. Someone could build the same experience by setting up a small model training and serving pipeline on AWS or GCP, with automated deployment and strict permissions. Trying agent-based automation inside a code hosting and messaging workflow would also match the preferred skills.

Worth showing

• GitHub Actions CI/CD pipelines
• Kubernetes and container deployments
• Cloud access control and security

This is our own read, written from the employer's posting and the company's Spotlight. The employer's posting is the source of truth for the role.

About Metacognition AI

An operating system for AI agents that sits beneath language models to control what they can do, released as open source.