MLOps & AIOps Engineer — AI Platform Reliability

Firmus Technologies Pty Ltd.; Sydney; Presencial; AUD 140.000 - 210.000; Jornada completa

Descripción de la vacante

Firmus Technologies in Sydney, Australia, seeks an experienced AI Engineer to design, develop and maintain the MLOps and AIOps foundations for Firmus AI Factory, our scalable AI platform.

You will own end-to-end ML workflows—from training to deployment—establish governance and evaluation gates, and promote models to production with robust, repeatable processes; you will also design evaluation pipelines for RAG and agentic applications ensuring data-driven, reproducible outcomes.

Formación

• Bachelor’s degree or higher in software or IT field.
• 3+ years in MLOps, ML platform, or production ML delivery pipelines.
• Strong understanding of ML lifecycle, versioning, promotion strategies and drift detection.

Responsabilidades

• Design end-to-end MLOps workflows: training → evaluation → registry → deployment → monitoring.
• Own model registry and promotion lifecycle with MLflow and release pipelines.
• Establish reproducibility, lineage, and versioning across model lifecycle.
• Design automated model quality gates for production (accuracy, latency, safety).
• Define SLOs/SLIs for AI platform features (training success, latency, availability).
• Develop production monitoring dashboards and incident response runbooks.

Conocimientos

MLOps

AIOps

Model registry

Evaluation automation

Distributed systems

Incident response

Educación

Bachelor’s degree in Software Engineering/CS/IT

Herramientas

MLflow

CI/CD pipelines

Monitoring tools

Descripción del empleo

Firmus Technologies in Sydney, Australia, seeks an experienced AI Engineer to design, develop and maintain the MLOps and AIOps foundations for Firmus AI Factory, our scalable AI platform.

You will own end-to-end ML workflows—from training to deployment—establish governance and evaluation gates, and promote models to production with robust, repeatable processes; you will also design evaluation pipelines for RAG and agentic applications ensuring data-driven, reproducible outcomes.