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Tier-1 employer and common DevOps role, but seniority and niche AI SRE specialization moderate applicant competition.
Highly domain-specific SRE, cloud, and applied AI requirements limit cross-industry transferability.
Tight filters: explicit 12+ years, specific cloud, SRE, and applied AI production experience required.
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Own reliability, performance, and operational integrity for high-visibility AI-infused products and platforms, ensuring they meet service-level objectives (SLOs) and error budgets.
Set production standards and lead admission of systems into production, including observability, performance, and resilience testing; maintain operational integrity across environments.
Mentor and lead engineering teams, drive continuous improvement of reliability KPIs through automation, incident postmortems, and collaborative cross-functional engagement.
12+ years of software engineering and site reliability engineering experience with large-scale, distributed, cloud-native systems.
3+ years experience in site reliability engineering including defining/owning SLIs, SLOs, SLAs, error budgets, on-call, and observability tooling.
3+ years experience with cloud-native engineering on hyperscalers (Azure, AWS, or GCP), including AI/ML services, Kubernetes, infrastructure-as-code, and multi-environment management.
Bachelor's degree in computer science, software engineering, data science, machine learning, or related discipline.
Senior-level SRE with deep expertise in cloud platform ownership, AI/ML workload operability, performance engineering, chaos engineering, and cost optimization.
Proven leader and mentor who has established and driven reliability standards, operational readiness, and production admission processes in complex environments.
Hands-on engineer comfortable with continuous learning, rapid experimentation, and collaborative interaction with multiple cross-functional teams including security and architecture.