AI Platform Engineer: MLOps & AIOps Lead

Firmus Technologies Pty Ltd.; Sydney; On-site; AUD 140,000 - 180,000; Full time

Job summary

Firmus Technologies Pty Ltd. is seeking an experienced AI Engineer to design, develop, implement, and maintain the MLOps and AIOps foundations for Firmus AI Factory, our scalable AI platform.

You will establish end-to-end workflows from model development to production, with governance, automated evaluation gates, and reliable promotion to production. You will also enable the Model Arena initiative by designing evaluation pipelines to ensure data-driven, reproducible, and production-safe model

Qualifications

• Bachelor's degree in software/CS/IT or closely related field.
• 3+ years in MLOps or production ML delivery roles.
• Deep understanding of ML software lifecycle including versioning and governance.
• Hands-on with MLflow Registry workflows and registry actions.

Responsibilities

• Design end-to-end MLOps workflows from training to deployment and monitoring.
• Own the model registry and promotion lifecycle with governance.
• Establish reproducibility and lineage across the ML lifecycle.
• Implement automated model quality gates for production systems.
• Define SLOs/SLIs for AI platform features and reliability metrics.
• Develop production monitoring dashboards and alerting integrations.
• Create on-call runbooks and lead postmortems for incidents.
• Instrument AI services with tracing and performance metrics.
• Integrate evaluation frameworks into CI/CD for testing and release.

Skills

MLOps

ML Platform

Software Eng

MLflow

CI/CD

Monitoring

Incident response

Distributed Systems

Data Collaboration

Model Evaluation

Education

Bachelor's degree in CS/IT

Tools

MLflow

PagerDuty

Slack

Git

Docker

Job description

Firmus Technologies Pty Ltd. is seeking an experienced AI Engineer to design, develop, implement, and maintain the MLOps and AIOps foundations for Firmus AI Factory, our scalable AI platform.

You will establish end-to-end workflows from model development to production, with governance, automated evaluation gates, and reliable promotion to production. You will also enable the Model Arena initiative by designing evaluation pipelines to ensure data-driven, reproducible, and production-safe model