Chaos engineering is a discipline that deliberately injects failures into a system (a server shut down, a slowed network, an unavailable dependency) to check that it holds up as expected, before a real incident exposes the weakness.
The approach is experimental: you state a hypothesis (“if a cloud zone goes down, the service stays available”), run the experiment on a limited scope, measure it with observability, then fix whatever gave way. Experiments start in pre-production, then move to production once the team is confident.
The practice was popularized by Netflix and its Chaos Monkey tool, which randomly shut down production instances. Tools such as Gremlin, Chaos Mesh, LitmusChaos and AWS Fault Injection Service have made it more accessible.
It is mostly found in SRE teams running high-availability services: payments, consumer platforms, critical infrastructure.
Why it matters when hiring
It is a skill of an experienced SRE or Platform Engineer, rarely a job in itself. The good signal: a candidate who describes a specific experiment, the hypothesis tested, what broke and what was fixed. The trap: confusing chaos engineering with load testing. There is no point requiring it in a company that has no observability or on-call rotation in place yet.