Hands-on experience in platform, infrastructure, or SRE with at least 3 years focused on cloud infrastructure or developer platforms.
Expertise in cloud-native architecture: Kubernetes at scale, managed cloud services, networking, identity federation, and multi-tenancy patterns across AWS, GCP, or Azure.
Proven ownership of complex, multi-month platform initiatives; you have part of them from whiteboard to production, managing ambiguity and technical risk throughout.
Proficient in IaC and GitOps fluency - Terraform or Pulumi, ArgoCD with experience standardising platform tooling and deployment patterns across engineering teams.
Hands-on experience with AI/ML infrastructure: model serving, inference pipelines, GPU resource management, or LLM integration patterns.
Experience in enterprise environments with familiarity with compliance frameworks such as SOC 2, ISO 27001, or GDPR.
Strong written communication; you write clear design docs, maintain useful ADRs, and can explain architectural decisions to both engineers and non-technical stakeholders.
Nice to Have:
LLM serving at scale - vLLM, Triton, Ray Serve or AI gateway design patterns.
You treat platform as a product and obsess over internal developer experience.
FinOps or GPU cost optimisation across large inference workloads.
You default to writing things down and creating shared technical context.
Building an internal developer platform (IDP) from scratch.
You push back on short-term thinking and advocate for the right long-term call.
Open-source contributions to platform or ML infrastructure tooling.
You are energised by ambiguity and build clarity where there isn’t any.
Qualifications:
Bachelor's or Master's degree in Computer Science, Information Technology, or a related field.
Job Description:
Lead the design and architecture of cloud-native platforms, manage Kubernetes at scale (AWS/GCP/Azure), including networking, identity federation, and multi-tenancy setups.
Own complex, multi-month platform initiatives end-to-end, from whiteboard design through production rollout, managing ambiguity and technical risk along the way.
Define and standardise IaC (Terraform/Pulumi) and GitOps (ArgoCD) practices to ensure consistent deployment patterns across engineering teams.
Build and maintain AI/ML infrastructure, design scalable systems for model serving, inference pipelines, GPU resource management, and LLM integration.
Ensure enterprise-grade compliance by aligning platform architecture with frameworks such as SOC 2, ISO 27001, and GDPR.
Write and maintain clear design docs and ADRs (Architecture Decision Records), communicating architectural decisions effectively to both engineers and non-technical stakeholders.
Treat the internal developer platform (IDP) as a product, continuously iterate to improve developer experience and adoption.
Collaborate cross-functionally to standardise platform tooling and drive its adoption across teams.
Explore and implement LLM serving frameworks (vLLM, Triton, Ray Serve) or AI gateway design patterns where applicable.
Drive GPU cost optimisation and FinOps practices for large-scale inference workloads.
Champion a culture of documentation and shared technical context across the team.