Platform Architect - Remote (LATAM)

Platform Architect (AI/ML Infrastructure, GCP-focused)

Level: Staff
Location: Remote in Latam (Brazil preferred)
Working hours: EST
Type: Full-Time (Payment in USD)
Reports to: Engineering Manager, Platform

Company Overview

is an AI native Labor Operating System redefining how the world’s largest enterprises manage frontline labor. Born out of a Stanford AI Lab, ReadyOn applies advanced AI and market design principles to one of the world’s hardest optimization problems: matching 2.7 billion frontline workers to the right shifts, in real time.

Frontline workers increasingly expect the flexibility and autonomy of gig platforms, while large employers face relentless pressure to control labor costs. ReadyOn bridges that gap with a system of action that predicts workforce demand, dynamically matches it to available employees, and automates thousands of staffing decisions across complex, multi site operations.

The platform is already proven at global scale, powering labor operations for some of the world’s largest enterprises, including F250 and F500 organizations spanning hundreds of thousands of employees and billions of dollars in annual labor spend. ReadyOn has demonstrated that scheduling was never the real problem. It was a symptom. The real challenge is dynamically matching people and work at scale.

Headquartered in San Francisco with over 100 employees, ReadyOn grew revenue 8x year over year in 2025, driven by multiple seven figure Fortune 250 deployments and a rapidly expanding pipeline.

Transform How Frontline Work Runs

Frontline labor can represent up to 40% of a company’s P&L, yet the systems managing this multi trillion dollar market were built around static schedules and manual processes.

ReadyOn is rejecting that paradigm. Staffing is not a scheduling problem. It is a real time supply and demand orchestration problem. Our AI native Labor Operating System is built from the ground up for AI agents to optimize labor in real time, much like ridesharing platforms match drivers and riders, but applied to frontline labor instead of fixed, one-size-fits-all schedules.

The Role

We're looking for a Platform Architect who can set the standard for how we build, ship, and operate ML and AI systems at scale. You sit at the intersection of ML infrastructure and SRE. You'll own the path from model and pipeline to reliable production service, and you'll bring DevOps rigor to systems that are historically under-engineered. Our immediate need is AI/ML infrastructure on Google Cloud.

This is not a ticket-processing role, and it's not a research role. You'll tackle hard problems: model serving reliability, inference cost and latency, reproducible pipelines, agentic workload operations. And you'll have the scope to solve them properly. Seniors here identify problems before they're asked, and raise the ceiling on what the platform can do.

What You'll Work On

• Build and operate model and inference serving infrastructure, managing latency, throughput, autoscaling, and reliability for real-time and batch inference across multiple tenants.

• Own the ML deployment lifecycle: model registry, versioning, promotion workflows, rollout strategies (canary, shadow, A/B), and safe rollback.

• Operate agentic and LLM workloads in production, managing inference providers and gateways, quota and throttling behavior (TPS/TUPS limits), guardrails, prompt/version management, and graceful degradation under load.

• Build reproducible, automated ML pipelines: training, evaluation, and deployment pipelines as code, with lineage and reproducibility built in.

• Extend infrastructure-as-code to ML systems, using Terraform patterns and multi-project design that bring ML infrastructure under the same standards as the rest of the platform.

• Operate GitOps for ML workloads, owning ArgoCD configuration and promotion workflows across environments and tenants.

• Run ML and AI workloads on multi-tenant Kubernetes (GKE), managing GPU/accelerator scheduling, workload placement, tenant isolation, and cost-aware capacity.

• Own ML reliability and observability: SLOs for inference services, model and data drift detection, performance regression monitoring, alert quality, on-call ergonomics, and runbook culture.

• Drive ML cost efficiency by right-sizing accelerators, managing committed-use and Spot VM capacity, and attributing inference cost across tenants and workloads.

• Use agentic coding tools for infrastructure and pipeline work: scaffolding environments, generating and reviewing IaC and pipeline code, and accelerating automation.

Must Have

• 5+ years in platform engineering, SRE, MLOps, or infrastructure, with meaningful time operating production systems at scale.

• Hands-on experience deploying and operating ML or AI workloads in production: serving, inference, or training infrastructure that real users depended on.

• Strong SRE/DevOps foundation. You've owned reliability for production services, defined and measured SLOs, run post-mortems, and driven measurable improvements.

• Deep Terraform expertise. You actively manage complex Terraform state, reusable modules, and multi-project configurations in production, with CI-driven plan/apply workflows.

• Strong GitOps background (ArgoCD or Flux in production). You understand declarative infrastructure management at depth and have opinions on how to do it well.

• Deep Kubernetes knowledge. You've operated clusters in production, dealt with real failure modes, and understand the system at the control plane level. Production GKE experience (Standard and/or Autopilot) strongly preferred.

• Strong GCP background: VPC networking, Compute Engine, IAM, Cloud Storage, and multi-project/organization design.

• Hands-on experience with GCP data services, especially BigQuery in production: partitioning and clustering, query cost and performance tuning, and dataset-level IAM. Familiarity with at least one of Dataflow, Pub/Sub, or Dataproc.

• Hands-on experience building and operating CI/CD pipelines (GitHub Actions, Cloud Build, GitLab CI, or equivalent), plus an understanding of how ML pipelines differ from standard application CI/CD.

• Automation-first thinking at a senior level. You implement systems that eliminate entire categories of manual work.

• Active user of agentic coding tools. You know how to direct them effectively, review their output critically, and use them to multiply your output.

• Strong communicator. You can articulate operational decisions, model performance trade-offs, and incident summaries clearly to engineers and leadership alike.

Nice to Have

• Experience with GPU/accelerator scheduling and node lifecycle management in production (e.g., GKE node auto-provisioning, GPU time-sharing, or equivalent).

• Experience operating LLM inference at scale, managing provider quotas/throttling (TPS/TUPS), gateways, caching, and guardrails (e.g., Vertex AI, Gemini API, or equivalent).

• Experience with ML pipeline and orchestration tooling such as Argo Workflows, Kubeflow, Cloud Composer/Airflow, Vertex AI Pipelines, or equivalent.

• Experience with model registries, feature stores, and experiment tracking (e.g., MLflow, Feast, or equivalent).

• Familiarity with model and data drift monitoring and ML-specific observability.

• Background in FinOps: inference cost attribution, committed use discount (CUD) and reservation planning, and accelerator capacity forecasting.

• Familiarity with data infrastructure such as object storage, CDC pipelines, or lakehouse patterns.

• Experience with multi-tenant infrastructure: isolation patterns, noisy neighbor mitigation, and tenant lifecycle management.

• Prior experience scaling ML or platform infrastructure at a startup moving toward enterprise-grade requirements.

What You Won't Find Here

A platform team that maintains the status quo. We're actively building: new scale requirements, new architectural domains, and an ML/AI footprint that's growing fast. Senior engineers here shape how the platform evolves, and the tools available to do it are better than they've ever been.