Platform Architect, AI/ML Infrastructure, GCP-focused

Job not on LinkedIn

🔥 4 minutes ago

🌐 Brazil, Argentina, +3 more countries – Remote

🇧🇷 Brazil – Remote 🇦🇷 Argentina – Remote 🇨🇴 Colombia – Remote 🇲🇽 Mexico – Remote 🇵🇪 Peru – Remote

⏰ Full Time

🟡 Mid-level

🟠 Senior

🔙 Backend Engineer

👻 Ghost score 14%

What it means: how likely this posting is sitting open without active hiring.How to read it: 60% or higher means we think this is a ghost. Lower scores are less of a worry.How we score it: age, reposts, open roles vs team size, how fast jobs close, and whether pay is listed.Fine print: an estimate from public data, not a judgment of the company. Real jobs can score high, so treat it as a hint, not a fact.

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Wizdaa

WebsiteLinkedInAll Job Openings

11 - 50 employees

💼 Consulting

📦 Logistics

📣 Marketing

Consulting • Logistics • Marketing

Wizdaa is a company that provides access to top-tier remote developers, specializing in helping startups build their dream development teams in the U. S. time zone. They offer a meticulous six-stage human and AI screening process to ensure access to the top 1% of engineering talent. Wizdaa's services include managing hiring processes, onboarding, payroll, benefits, and taxes, allowing startups to focus on core business matters. Known for competitive rates averaging $30/hour, Wizdaa emphasizes cultural fit, technical excellence, and English fluency among their developers. The company prides itself on delivering tailored, cost-effective solutions that maximize startups' runway and success. They also offer insights into leveraging AI tools and aligning remote teams with U. S. time zones to boost productivity.

📋 Description

• Build and operate model and inference serving infrastructure for real-time and batch inference across multiple tenants • Manage latency, throughput, autoscaling, and reliability • Own the ML deployment lifecycle, including model registry, versioning, promotion workflows, rollout strategies, and safe rollback • Operate agentic and LLM workloads in production, including inference providers, gateways, quotas, throttling, guardrails, prompt/version management, and graceful degradation • Build reproducible, automated training, evaluation, and deployment pipelines as code • Extend infrastructure-as-code practices to ML systems using Terraform and multi-project design • Operate GitOps for ML workloads and own ArgoCD configuration and promotion workflows • Run ML and AI workloads on multi-tenant Kubernetes/GKE, managing GPU scheduling, workload placement, tenant isolation, and capacity • Own ML reliability and observability, including inference SLOs, model/data drift detection, regression monitoring, alert quality, on-call ergonomics, and runbooks • Drive ML cost efficiency through accelerator right-sizing, committed-use and Spot VM capacity management, and cost attribution • Use agentic coding tools to scaffold environments, generate/review IaC and pipeline code, and accelerate automation • Identify platform problems proactively and shape platform evolution

🎯 Requirements

• 5+ years in platform engineering, SRE, MLOps, or infrastructure, including meaningful time operating production systems at scale • Hands-on experience deploying and operating ML or AI workloads in production • Strong SRE/DevOps foundation, including ownership of reliability, SLOs, post-mortems, and measurable improvements • Deep Terraform expertise, including complex state, reusable modules, multi-project configurations, and CI-driven plan/apply workflows • Strong GitOps background with ArgoCD or Flux in production • Deep Kubernetes knowledge and production cluster operations, including control-plane-level troubleshooting • Production GKE experience is strongly preferred • Strong GCP background, including VPC networking, Compute Engine, IAM, Cloud Storage, and multi-project/organization design • Hands-on production BigQuery experience, including partitioning, clustering, query cost/performance tuning, and dataset-level IAM • Familiarity with Dataflow, Pub/Sub, or Dataproc • Hands-on experience building and operating CI/CD pipelines • Understanding of differences between ML pipelines and standard application CI/CD • Senior-level automation-first thinking • Active use of agentic coding tools • Strong communication skills • Experience with GPU/accelerator scheduling and node lifecycle management is nice to have • Experience operating LLM inference at scale is nice to have • Experience with ML pipeline and orchestration tooling is nice to have • Experience with model registries, feature stores, and experiment tracking is nice to have • Familiarity with model and data drift monitoring and ML-specific observability is nice to have • FinOps background is nice to have • Familiarity with data infrastructure is nice to have • Experience with multi-tenant infrastructure is nice to have • Prior startup scaling experience is nice to have • Bachelor's Degree or equivalent experience • Degree in IT or Computer Science or equivalent experience • English conversational skills

🏖️ Benefits

• Payment in USD • Remote work arrangement • Working hours aligned with EST time zone