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Senior Cloud AI Engineer, Melbourne (Hybrid)
Build secure enterprise AI and cloud platforms for Services Australia. Lead MLOps, infrastructure, integration and DevSecOps across hybrid environments.
विवरण
- Build and improve enterprise AI, machine learning and generative AI platforms, applications and infrastructure
- Lead secure, scalable engineering across public cloud, on-premises and hybrid environments
- Create cloud-native architectures, automated infrastructure and enterprise AI engineering frameworks
- Build and maintain AI and machine learning solutions, covering model deployment, inference, evaluation, monitoring and lifecycle management
- Design and support highly available cloud infrastructure, including capacity planning, resilience and operational performance
- Lead complex software development, systems integration, platform deployment, migration and technical implementation
- Develop Infrastructure as Code, CI/CD pipelines, automated deployments and secure software delivery processes
- Apply DevSecOps and secure-by-design practices in line with Australian Government security, privacy and governance requirements
- Assess technical requirements and solution options, then recommend architectures, technologies and implementation approaches
- Diagnose and resolve complex issues across applications, platforms, infrastructure and enterprise services
- Guide multidisciplinary delivery teams with technical leadership and specialist advice
- Set engineering standards, reusable architecture patterns, documentation and operational procedures
- Work with internal and external stakeholders to align solutions with business needs and organisational goals
- Advance innovation, continuous improvement, technical governance and adoption of emerging technologies
- Prepare systems for operation and support reliability, releases, knowledge transfer and ongoing service improvement
आवश्यकताएँ
- Hold an active Baseline clearance
- Bring senior-level experience in AI software engineering, cloud infrastructure, platform engineering, DevOps, MLOps, solution architecture or enterprise systems integration
- Have strong experience in one or more relevant areas of AI engineering, software development, MLOps, cloud infrastructure, platform engineering or DevOps
- Demonstrate strong programming and software engineering skills, particularly in Python, automation, APIs and enterprise application integration
- Have experience delivering enterprise AI and machine learning solutions, including model deployment, inference, evaluation, monitoring and lifecycle management
- Have experience with LLMs, generative AI, RAG, embeddings, vector databases, AI agents and foundation models
- Have experience with relevant AI frameworks and tools such as Hugging Face, vLLM/TGI, Kubeflow, AWS SageMaker, Azure AI or Azure Machine Learning
- Have experience with AWS, Microsoft Azure, on-premises or hybrid cloud infrastructure
- Have experience with Kubernetes, OpenShift, OpenShift AI or comparable platforms
- Have experience with Docker or Podman, container image management, registries, vulnerability scanning and secure software supply chains
- Have experience with infrastructure as code and automation tools such as Terraform, Ansible, GitOps, Argo CD, GitLab, GitHub Actions or Azure DevOps
- Have experience with Linux administration, networking, storage, DNS, TLS, load balancing, ingress, certificates and cloud or on-premises connectivity
- Have experience with cloud availability, capacity planning, performance optimisation, disaster recovery and operational resilience
- Have experience with monitoring and observability tools such as Prometheus, Grafana, AWS CloudWatch or Azure Application Insights
- Have experience deploying and migrating enterprise platforms, installing systems, troubleshooting infrastructure and managing problems
- Have experience applying FinOps principles
- Have experience with NVIDIA GPU infrastructure, CUDA fundamentals, GPU scheduling and AI workload optimisation
- Have experience with OpenShift AI pipelines, model registries, model serving and S3-compatible object storage
- Have experience with REST APIs, OAuth/OIDC, API gateways, streaming platforms and data lakes
- Have experience with DevSecOps, RBAC, secrets management, network security, vulnerability management and secure platform configuration
- Understand Australian Government security frameworks, privacy obligations, data sovereignty and regulatory compliance
- Demonstrate technical leadership and the ability to provide specialist advice and guidance across complex initiatives
- Work autonomously, resolve complex technical challenges and influence technical decisions
- Demonstrate experience designing complex enterprise technology solutions and implementing secure, scalable, maintainable and supportable systems
- Have experience leading or contributing to complex software development, systems integration, infrastructure implementation, automated deployment or technology migration
- Have experience selecting, implementing and improving engineering methodologies, development tools, automation practices and technical standards
- Understand secure-by-design principles, cybersecurity requirements, technical risk management and governance obligations
- Have strong written and verbal communication skills
- Provide an updated CV, preferably 3–5 pages
- If shortlisted, provide a one-page pitch of no more than 5,000 characters addressing the relevant selection criteria
लाभ
- A hybrid work-from-home arrangement may be considered after the first two weeks, typically requiring at least two days onsite each week
- Contract extensions may be available, up to a maximum term of 24 months
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