Senior ML Engineer — Petabyte-Scale MLOps in Cloud

Easygo; City of Melbourne; On-site; AUD 160,000 - 230,000; Full time

Benefits offered by this job

Free coffee and beverages

Catered breakfast and team events

Snack walls and modern office spaces

F1 simulator and gaming lounges

Wellbeing programs and massages

Job summary

Easygo in Melbourne is seeking a Senior Machine Learning Engineer to advance MLOps across the organisation. You will design scalable ML infrastructure in AWS/Azure/GCP, implement IaC with Terraform, and build CI/CD pipelines for model deployment.

You will collaborate with data scientists to identify infra needs, monitor models and mentor junior engineers, helping deliver high-value ML projects at scale.

Qualifications

• Experience in MLOps, DevOps, Data Engineering or cloud infra roles.
• Bachelor’s degree in CS, Engineering or related field.
• Expert proficiency with Terraform.
• Hands-on with AWS, Azure or GCP.
• Strong CI/CD experience for ML workloads.
• Proficiency with containerisation (Docker, Kubernetes).
• Advanced Python and scripting for infra automation.

Responsibilities

• Design, implement and maintain end-to-end ML infrastructure and automation.
• Drive cloud infrastructure decisions for large-scale ML workloads using IaC.
• Build and maintain CI/CD pipelines for ML model deployment.
• Develop monitoring, alerting and logging for model reliability and compliance.
• Collaborate with data scientists and stakeholders to align infra needs.
• Mentor junior MLOps engineers and data scientists on best practices.

Skills

MLOps

DevOps

Data Engineering

Cloud infrastructure

Education

Bachelor’s degree in Computer Science or related field

Tools

Terraform

Docker

Kubernetes

AWS

Azure

GCP

Job description

Easygo in Melbourne is seeking a Senior Machine Learning Engineer to advance MLOps across the organisation. You will design scalable ML infrastructure in AWS/Azure/GCP, implement IaC with Terraform, and build CI/CD pipelines for model deployment.

You will collaborate with data scientists to identify infra needs, monitor models and mentor junior engineers, helping deliver high-value ML projects at scale.