Principal Platform Engineer — Kubernetes & Cloud Infrastructure

What you'll own

• Production Kubernetes (EKS) clusters: capacity planning, node group strategy, gen-AI workload isolation, blast-radius containment.
• AWS infrastructure end-to-end: RDS, DMS, Kafka (MSK), ECR, networking, IAM, multi-region deployments (including Ireland for EU data residency).
• Infrastructure-as-code in Terraform — modules, environments, drift management, peer review.
• CICD pipelines (Jenkins, GitHub Actions, or your recommended replacement) — fast, reliable, secure builds for backend and frontend services.
• Observability: Grafana dashboards, Prometheus metrics, log pipelines, on-call alerting, SLO definition.
• Cost optimization. AWS spend is one of our top three variable costs. Reducing it by 20% is a tangible objective for this seat.
• Security posture: secrets management (Consul/Vault), IAM hygiene, vulnerability patching, support for SOC 2 and ISO 27001 audit cycles.
• Architecture leadership on the self-service infrastructure roadmap: how we onboard a customer without human intervention and scale to 10x our current tenant count.
• Documentation and runbooks that let the rest of the engineering team operate the platform when you're unavailable.

Must-haves

• 8+ years of platform, infrastructure, SRE, or DevOps experience, with at least 3+ years operating production Kubernetes at scale.
• Deep AWS expertise across compute, storage, networking, data services, and IAM.
• Production fluency with Terraform, Docker, Linux, and CI/CD systems.
• Track record of architectural decisions that materially improved reliability, cost, or developer velocity — with specific, measurable outcomes you can point to.
• Comfort operating as a senior IC who sets technical direction across teams without formal authority.
• Strong written communication — runbooks, architecture decision records, post-incident reviews.
• Willingness to be in-office Tuesday through Thursday in Denver.

Nice-to-haves

• Production experience supporting generative AI or ML workloads (GPU node groups, vector databases, model serving).
• Experience with Qdrant, Pinecone, Weaviate, or other vector stores in production.
• PostgreSQL operational depth — replication, performance tuning, backup/restore.
• Experience scaling a multi-tenant SaaS platform from ~100 customers to ~1,000.
• SOC 2 Type II and ISO 27001 audit experience.
• Familiarity with event-driven architectures (Kafka, Kinesis, or equivalent).