AI Engineer, AI & Applications

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AI Engineer, AI & Applications in Australia

Firmus Technologies
Full time
Email

Australia
10/13/2026
Report

Sydney

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.

Firmus Technologies

Firmus Technologies delivers cutting‑edge digital solutions that empower businesses to innovate and scale efficiently. Our team blends industry expertise with agile development practices to craft customized software that meets each client’s unique needs. By fostering collaboration and continuous ...

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