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Mid-level common title, metro location, and generalist platform requirements create high competition.
Platform and Kubernetes skills transfer across industries but require specific cloud and distributed-systems experience.
Explicit 5+ years requirement plus mandatory Kubernetes and distributed-systems skills produce medium strictness.
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Design, build, and operate distributed platform infrastructure running AI and data workloads (Notebooks, Jobs, AI Agents, Model-serving) across multi-cloud regions.
Own services managing container pools, resource scaling, routing, RBAC, billing, observability, and secure network access between clusters.
Lead cross-functional technical projects and enforce engineering best practices to ensure scalable, reliable, and secure cloud infrastructure operations.
5+ years experience working on SaaS products.
Experience with distributed systems programming using Golang, Python, or Rust.
Experience working with Kubernetes and containerized services.
Bachelor’s degree in Computer Science or equivalent practical experience.
Experienced in building and operating production distributed platforms that support real-time AI and data workloads at scale.
Capable of leading complex technical initiatives and mentoring engineering teams.
Familiar with cross-cloud infrastructure (AWS, Azure, GCP) and developer, compute, or serverless platforms.