Reliability Engineering

Reliability Engineering is the discipline of ensuring that systems, services, and products perform their intended functions without failure over a specified period and under defined conditions. In software and AI contexts it is commonly practiced as Site Reliability Engineering (SRE), which applies software engineering principles to infrastructure and operations problems. Core concerns include measuring reliability through SLIs and SLOs, managing error budgets, automating toil, and building resilient distributed systems.

As AI companies scale inference infrastructure and multi-region deployments, reliability engineers are essential for keeping models and APIs available at the 99.9%+ uptime that paying customers demand. The rise of large-scale GPU training clusters — where a single hardware failure can waste thousands of GPU-hours — has made reliability engineering a first-class concern for every major AI lab. Roles spanning MLOps, platform engineering, and AI infrastructure all increasingly require SRE skills such as SLO design, chaos engineering, and incident management.