AI Engineer, AI & Applications

Responsibilities

• Design, develop, and maintain end-to-end MLOps workflows across development, staging, and production environments.
• Own MLflow Model Registry workflows, including stage and alias strategies, approvals, access control, rollback readiness, and release-pipeline integration.
• Establish model and dataset reproducibility, lineage, artifact traceability, and repeatable evaluation processes.
• Develop automated production quality gates covering accuracy, latency, cost, safety, and reliability.
• Define SLOs and SLIs for training jobs, inference services, RAG retrieval, availability, and cost.
• Build monitoring dashboards for model performance, data drift, and operational health, integrating alerts with PagerDuty and Slack.
• Create on-call runbooks, triage procedures, and incident-response processes for AI services.
• Instrument AI services with request traces, GPU metrics, retrieval performance, and communication diagnostics.
• Integrate benchmarking, RAGAS, and LLM-as-judge evaluation frameworks into CI/CD and release workflows.
• Partner with ML engineers, data scientists, platform teams, application engineers, and operations teams on production reliability and L2/L3 incident response.

Requirements

• Bachelor’s degree or higher in Software Engineering, Computer Science, Information Technology, or a closely related field.
• At least 3 years of experience in MLOps, ML platform, software engineering, or production engineering roles.
• Hands-on ownership of production machine-learning delivery pipelines.
• Deep understanding of model versioning, promotion strategies, evaluation automation, governance, deployment, monitoring, and drift detection.
• Hands-on experience with MLflow Model Registry workflows, including stages or aliases, approvals, traceability, and release-pipeline integration.
• Experience designing and operationalising model evaluation systems with metrics standards, orchestration, logging, and reproducibility.
• Strong software engineering and production fundamentals covering metrics, logs, traces, alerting, incident response, and reliability practices.
• Familiarity with CI/CD pipelines, model packaging, deployment automation, and software testing.
• Understanding of distributed systems, resource management, and failure modes in training and inference environments.
• Ability to define SLOs, error budgets, monitoring practices, and postmortem-driven improvements.
• Ability to communicate complex ML and systems issues to technical and non-technical stakeholders.

Benefits

• Full-time position based in Sydney, Australia.
• Opportunity to work on sustainable AI infrastructure and Firmus AI Factory.
• Inclusive workplace encouraging applications from candidates of all backgrounds.