The posting
Role Summary
Firmus Technologies is seeking an experienced AI Engineer to design, develop, implement and maintain the MLOps and AIOps foundations for Firmus AI Factory, our AI platform, to make it trustworthy, repeatable and scalable.
This is a pioneering role where you will establish the end-to-end MLOps workflows, turning model development into a disciplined software release process with clear governance, automated evaluation gates and reliable promotion to production.
You will also enable our Model Arena initiative by designing, developing and operationalising the evaluation pipelines and standards so model choices for RAG and agentic applications are data-driven, reproducible and production-safe.
You are also responsible for the design, testing, maintenance and reliability of Firmus AI Factory AI features, including training jobs, inference services and RAG systems. You will define quality gates, model promotion workflows, production monitoring and incident response procedures.
Your role is to ensure AI platform features are engineered to be as trustworthy as core infrastructure — fast, reliable, maintainable and observable.
You will work across the team, partnering with engineers on CI/CD gates, data scientists on quality metrics, and operations teams on L2/L3 incident response.
Key Responsibilities
- Design, develop, implement and maintain end-to-end MLOps workflows: training → evaluation → registry → deployment → monitoring → retraining/retirement in dev/staging/production environments, with clear standards and ownership boundaries.
- Own the model registry and promotion lifecycle using MLflow, including stage/alias strategy, approvals, environment separation, access control, rollback readiness and integration with software release pipelines.
- Establish reproducibility and lineage across the model lifecycle, including versioned code/configuration, artifact traceability, dataset/version references and repeatable evaluation runs.
- Design, develop and implement automated model quality gates for production, including accuracy, latency, cost, safety and reliability requirements.
- Define SLOs/SLIs for all AI platform features, including training job success rate, inference latency p99, RAG retrieval accuracy, availability and cost metrics.
- Develop and maintain production monitoring dashboards to track model performance, data drift and operational health, and integrate monitoring outputs with alerting tools such as PagerDuty and Slack.
- Create on-call runbooks and triage procedures for AI service incidents and lead postmortem-driven improvements to improve software reliability and maintainability.
- Instrument AI services for debugging, including request traces, GPU metrics per model, retrieval performance and communication bottlenecks.
- Integrate evaluation frameworks, including benchmarking, RAGAS and LLM-as-judge, into CI/CD pipelines to support automated testing, deployment and release management.
Skills & Experience
- Bachelor’s degree or higher qualification in Software Engineering, Computer Science, Information Technology or a closely related field.
- 3 + years in MLOps, ML platform, software engineering or production engineering roles with hands-on ownership of production ML delivery pipelines.
- Deep understanding of the ML software lifecycle, including model versioning, promotion strategies, evaluation automation, governance, deployment strategies, monitoring and drift detection.
- Hands-on experience with MLflow Model Registry workflows, including stages/aliases, approvals, traceability and integration of registry actions into software release pipelines.
- Experience designing, developing and operationalising model evaluation systems, including metrics standards, orchestration, logging and reproducibility.
- Strong software engineering and production fundamentals, including metrics, logs, traces, alert design, incident response and reliability practices.
- Familiarity with CI/CD pipelines, model packaging, deployment automation and software testing, with the ability to collaborate across ML engineers, platform/SRE and application teams to turn requirements into robust workflows.
- Understanding of distributed systems, resource management and failure modes in training and inferencing environments.
Key Competencies
- Production Ownership: comfortable owning services in production; proactive about monitoring, alerting, and preventing issues.
- Reliability Engineering: can define SLOs, error budgets, and blameless postmortem culture.
- Cross-Functional Leadership: works with ML engineers, data scientists, and platform teams; unblocks reliably.
- Incident Response: triage skills, root cause analysis, systemic thinking (not just fighting fires).
- Programmatic automation reduces toil and makes the right path with a balanced rigor with speed.
- Communication: explains complex ML/systems issues clearly to both technical and non-technical stakeholders.
Location & Reporting
- Location Sydney, Australia
- Reporting to Head of AI & Applications
Employment Basis
Full-time
Diversity
At Firmus, we are committed to building a diverse and inclusive workplace. We encourage applications from candidates of all backgrounds who are passionate about creating a more sustainable future through innovative engineering solutions.
Join us in our mission to revolutionize the AI industry through sustainable practices and cutting-edge engineering. Apply now to be part of shaping the future of sustainable AI infrastructure.
