Systems Architect

Essential Experience:

• Proven results designing scaled end to end systems (not just components, or end services).
• An expert of infrastructure, cloud, or platform related Architecture/Engineering.
• Exceptional experience designing Kubernetes and cloud native solutions at scale.
• Commercial aptitude to enable performance, cost, time trade-offs and evolutions.
• Proven experience defining target architectures and technology roadmaps.
• Strong understanding of Infrastructure-as-code, automation, GitOps.
• Experience of Open Source stacks including scale-out storage/object stores (Ceph).

One or more would be an advantage:

• Contribution to open-source projects in the IaaS/PaaS space.
• Experience of building and consuming significant cloud services.
• Commercial success of turning technology into a commercially successful product offering.
• AI/ML System design and consumption practises.

We are seeking a Systems Architect to define, design, and govern the architecture of our Kubernetes-based AI infrastructure. This role is responsible for translating business, customer, and regulatory requirements into scalable platform designs and technical specifications that guide platform engineering and operations teams.

Work closely with Product Managers, Platform Engineers, ML engineers, and leadership to ensure the platform is secure, performant, resilient, and future-proof, enabling AI workloads at national-scale.

This is an opportunity to define the end-to-end architecture of our AI platform, defining how Kubernetes, GPU infrastructure, networking, storage, and automation components integrate into a cohesive system.

This is a design authority role, responsible for:

• Platform blueprints
• Technical standards
• System-level decisions
• Long-term scalability and evolution

Provide clear specifications and boundaries that enable Platform Engineers to build and operate the platform effectively.

Key Responsibilities:

• Platform Architecture & Design Authority:
• Produce high-level and low-level designs (HLDs/LLDs) for platform components.
• Establish reference architectures for multi-tenant AI workloads.
• Specify system and integrations across Kubernetes, IAM, PaaS, Observability and automation, ITSM, Security, Billing – the complete cloud service provider stack.
• Define solutions for multi-tenant SaaS offerings for GPU cloud related offerings.

Scalability, Reliability & Performance:

• Provide inputs for SLOs/SLAs across: Infrastructure, SaaS services, Storage services.
• Architect: Capacity planning (compute + storage), Performance optimisation across workloads.