Senior Platform Architect

Role overview Set technical direction for reliable cloud platforms that support backend services, AI workloads, and multi-tenant products. This senior role combines platform engineering and SRE: you will design infrastructure, operate production systems, improve deployment and incident practices, and address performance and cost challenges as the platform grows. The listed setup is remote in Latin America, with USD payment and working hours aligned to Eastern Time.

Responsibilities - Build and operate scalable infrastructure for real-time and batch workloads, including production LLM and agent systems and their external integrations. - Design safe delivery workflows with versioning, staged rollouts, monitoring, and rollback; maintain GitOps deployments and CI/CD pipelines. - Extend reusable Terraform patterns and run Kubernetes workloads, managing scaling, capacity, placement, and tenant isolation. - Improve observability and reliability through metrics, logs, traces, SLOs, alerts, and incident response practices. - Identify ways to reduce infrastructure, compute, API, and AI workload costs while improving performance. - Develop reusable platform services, APIs, automation, and data pipelines; use AI coding tools with careful review of generated work.

Requirements - 5+ years in platform engineering, SRE, or infrastructure, including meaningful production operations at scale. - Strong reliability practice: ownership of production services, SLOs, postmortems, and measurable improvements. - Deep production experience with Terraform, complex state, reusable modules, and CI-driven plan and apply workflows. - Strong GitOps experience with ArgoCD or Flux and hands-on Kubernetes operations, including production failure modes. - Cloud infrastructure experience in AWS, Azure, or GCP, covering networking, compute, identity, storage, and multi-account or multi-project design. - Experience building CI/CD pipelines and automating away recurring manual work; active use of agentic coding tools with critical review.

Nice to have - Experience with ML platforms and pipelines, model registries, feature stores, experiment tracking, or ML observability. - FinOps experience, data infrastructure knowledge, or multi-tenant platform design. - Experience scaling ML or platform infrastructure in a startup environment toward enterprise needs.