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DevOps & Infrastructure
ReadyOn
ReadyOn.AI is building an AI-native Labor Operating System that helps large enterprises schedule and match frontline workers to shifts in real time, drawing on research from the Stanford AI Lab. The company is hiring a staff-level Platform Architect to own its machine learning and AI infrastructure on Google Cloud, from model-serving reliability to running agentic workloads in production. The position is fully remote anywhere in Latin America, with Brazil preferred, and is paid in US dollars. Working hours follow US Eastern time to overlap with the core team. It suits a platform or SRE engineer who wants to build reproducible, observable ML systems at production scale.
4 more roles like this one.
Build and operate model-serving infrastructure with clear latency and reliability targets. Own the ML deployment lifecycle including versioning, rollout strategy and rollback. Run LLM and agentic workloads in production and build reproducible ML pipelines as code. Extend Terraform infrastructure-as-code to ML systems and operate GitOps for ML via ArgoCD. Run multi-tenant Kubernetes clusters with GPU scheduling and tenant isolation. Establish SLO-based reliability and observability, drive accelerator cost efficiency, and use agentic coding tools for infrastructure automation.
5 or more years in platform engineering, SRE, MLOps or infrastructure, with meaningful time operating production systems at scale. Hands-on experience deploying and operating ML and AI workloads. Strong SRE and DevOps foundation with SLO ownership. Deep Terraform and GitOps expertise and deep Kubernetes and Google Cloud knowledge, including services such as BigQuery, Dataflow, Pub/Sub or Dataproc. CI/CD pipeline experience, an automation-first mindset, active use of agentic coding tools, and strong communication with technical and leadership audiences. Based in Latin America and able to work US Eastern hours.
Salary not disclosed
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This job title typically pays $105,210 to $171,980 a year (median $135,980) for similar roles in the US.
Estimate from US Bureau of Labor Statistics data, not provided by the employer.