Senior MLOPs Platform Engineer
AUD 80K-100K (estimate) Senior-level Full Time
Tasks
- Build GPU accelerated distributed training
- Build developer experience tooling
- Build large scale batch inference
- Build real time model serving
- Deploy and govern models
- Design scalable ML infrastructure
- Develop LLM platform for generative AI products
- Develop production Python libraries and services
- Drive ML reliability with SLAs and SLOs
- Implement ML observability
- Manage CI/CD pipelines
- Manage Docker tooling
- Operate ArgoCD for deployments
- Own end to end ML platform projects
- Provide tech lead guidance
- Run experiments and optimize cost
Perks/Benefits
- Annual flu vaccinations
- Cycling showers
- Hybrid flexibility
- In-office lunch
- Nearmap subscription
- On-site two days per week
- Snacks
- Wellbeing and technology allowance
- Wellbeing day off
Skills/Tech-stack
AWS | Amazon EKS | ArgoCD | Arize AI | Batch inference | CI/CD | Cloud platform | Distributed Training | Docker | Evidently | GPU Training | Google Cloud | Google Cloud Platform | Google GKE | Grafana | Infrastructure as Code | Kubernetes | LLM Deployment | Language Models | Large Language Models | Learning operations | MLflow | Machine Learning | Machine Learning Operations | Model Serving | Observability | Prometheus | Python | Ray Serve | Real Time | Real-time APIs | Terraform | Weights and Biases | “as-code”
Education
Bachelor of Engineering | Bachelor of Science | Master of Science | PhD
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