Omnis Partners are hiring on behalf of an enterprise-focused AI consultancy helping large organisations move AI systems from prototype into safe, scalable production. They are looking for a Lead Platform Engineer with deep, hands-on experience designing and operating cloud-native platforms at scale. This is a hands-on role, focused on production reliability, Kubernetes, platform automation and building self-service infrastructure for engineering teams. The role You will help design, build and operate secure, reliable platform infrastructure for enterprise AI workloads. The work will involve: Building and operating Kubernetes-based platforms Designing self-service infrastructure for internal engineering teams Improving reliability, resilience, observability and deployment safety Keeping platform tooling secure, current and production-ready Working closely with engineering teams and client stakeholders Making pragmatic trade-offs around reliability, cost, security and delivery speed Technical experience ~12+ years’ professional engineering experience ~ Strong hands-on Kubernetes experience ~ Terraform as the primary IaC tool ~ Helm, Kustomize, Pulumi or similar ~ ArgoCD / FluxCD ~ Cert-Manager, ExternalDNS, Cluster Autoscaler ~ Bash / shell scripting ~ Python or Go for tooling and automation ~ Secrets management and supply-chain security ~ Cosign, SBOMs, vulnerability scanning or similar ~ DNS, TLS, load balancers and service mesh concepts ~ Strong production reliability and operational ownership Useful background Platform engineering, SRE, DevOps or infrastructure engineering Experience building platforms for internal developers Regulated, enterprise, financial services, public sector or security-conscious environments Exposure to private cloud, sovereign cloud, on-prem or non-standard cloud environments would be useful The person You should be a senior hands-on engineer who thinks in terms of platforms, not just infrastructure tasks. You’ll suit this role if you are pragmatic, security-aware, collaborative with product and engineering teams, and comfortable keeping infrastructure reliable while enabling teams to move quickly. AI/ML experience is not required, but interest in the platform and reliability challenges behind production AI systems would be helpful.
