Group Owner Cloud AI Infrastructure

Employment Type

Closing Date

Job Title

Job Summary

Job Description

Telstra operates one of Australia’s largest cloud and AI infrastructure environments, supporting Connected Future 30 workloads across customer, network, digital, engineering, cyber, data and AI domains. As Telstra scales AI use cases and autonomous agents, it needs sovereign, secure, resilient, low latency, cost-effective, lifecycled and managed AI infrastructure that tenants can consume and trust as a service.

The role owns the strategy, roadmap, DevSecOps, product lifecycle and developer experience for AI infrastructure, embedding as code, Telstra’s approved security, risk, privacy, resilience and responsible AI policies so workloads operate within Telstra guardrails. The role ensures Telstra’s AI infrastructure is soveriegn, secure, resilient, and lifecycled – providing the capabilities and capacity the business needs at scale, with the required trust, availability, elasticity, performance and industry-leading unit economics. It translates enterprise demand, supplier capability, technology innovation and commercial constraints into prioritised roadmaps, investment cases, capacity plans and measurable outcomes. The role ensures our AI infrastructure operates within Telstra’s defined security and operations processes and SLAs.

Key Accountabilities

• Set the strategy, roadmap and operating model for Cloud AI Infrastructure Services, aligned to Connected Future 30, AI adoption and Autonomous Cloud outcomes.
• Own the AI infrastructure product portfolio across GPUs, accelerators, storage, networking, cloud and self-hosted, registry, telemetry, automation, AI serving and token supply/capacity.
• Provide secure, resilient, observable and cost-effective AI infrastructure services that meet Telstra’s sovereignty, security, latency, data protection and intellectual property guardrails
• Ensure Telstra can supply the AI infrastructure and token capacity the business needs at scale, with at industry-leading unit cost.
• Create an AI infrastructure-as-a-service consumption model with standard patterns, APIs, service registry, documentation, onboarding journeys and support models that make AI infrastructure easy to adopt and reuse.
• Embed approved security, risk, privacy, resilience and responsible AI requirements into platform controls, access patterns, policy enforcement, telemetry and auditability.
• Own AI workload placement across GPUs, telco cloud, NEO cloud, sovereign cloud, public cloud and AI gateways, using NPT sovereignty principles and risk-tiered sovereign cloud requirements (P1–P7) to balance performance, latency, sovereignty, resilience, security, compliance and unit cost.
• Design sovereign resiliency patterns so critical AI capability can continue operating during loss of international connectivity, hyperscaler disruption or provider withdrawal.
• Run AI platform operations to the standard required for network-critical workloads, including defined service tiers, mission-critical SLOs, on-call and incident response, redundancy, disaster recovery testing, service health reporting and operational readiness controls.
• Own tool convergence across AI infrastructure layers, using the September tooling review as a baseline and making scale, converge, enhance or retire decisions for hosting, inference, gateways and related platform capabilities.
• Lead a multidisciplinary technology, engineering and product organisation, building a product engineering culture focused on developer experience, reusable platform capability, measurable value and continuous improvement.

Must Haves

• Senior technology leadership across large-scale AI infrastructure, cloud, platform engineering or security domains.
• Strong practical expertise in GPU and accelerator platforms, model serving, and AI models along with modern DevSecOps, Infrastructure as Code and Site Reliability Engineering practices.
• Practical experience with open standards and multi-vendor resilience, including OpenTelemetry, OpenAI-compatible APIs and Model Context Protocol (MCP), to reduce platform lock-in and differentiate candidates from general cloud leaders.
• Familiarity with AI runtime security, including controls for model serving, agent execution, telemetry, policy enforcement and secure consumption of AI infrastructure services.
• Proven ability to lead technology transformation, including as-a-service adoption, product-engineering ways of working and internal customer engagement for AI, cloud or platform services.
• Strong commercial and operational capability across capacity planning, unit economics, supplier strategy, investment trade-offs and lifecycle decisions.
• Experience managing ecosystem and supply-chain risk across hyperscalers, GPU capacity, model providers and strategic technology partners
• Strong enterprise influence, with the confidence to challenge, align and mobilise senior stakeholders across technology, Data & AI, cyber, product, finance, risk, legal and strategic partners.
• Resilience, adaptability and sound judgement in ambiguous, fast-moving environments, with a leadership style that builds transparency, accountability and early escalation of risks.
• Demonstrated ability to build high-performing teams with high psychological safety, clear accountability and sustained performance.
• Ability to attract, retain and develop scarce technical talent in a fast-moving technology domain.

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