Engineering and infrastructure

AI observability, reliability and FinOps

AI observability, reliability and FinOps is the work of watching AI services in production and tying uptime, quality and compute spend to real outcomes. Companies need it because model-dependent products fail and cost money in ways ordinary monitoring misses.

More companies now run models and agents for real customers, and the first bills and outages are arriving. Someone has to explain what went wrong and what each task costs. These hires are tricky because the skills sit in different places: SRE, performance engineering, cloud operations and FinOps. Few people have covered all of them. Monitoring also only shows what was instrumented, so a strong hire is honest about what they cannot see.

Hiring now 3Emerging 2

Roles in this family

5 roles

Hiring nowAI Site Reliability EngineerAn AI Site Reliability Engineer keeps services that depend on models available and recoverable under real traffic. They matter because customers…EmergingAI Observability EngineerAn AI Observability Engineer instruments model and agent workflows so teams can see what is happening in production. It matters because failures…Hiring nowAI FinOps AnalystAn AI FinOps Analyst measures what each useful AI task costs and finds ways to bring it down without hurting quality. They give the company a clear…Hiring nowAI Capacity Planning EngineerAn AI Capacity Planning Engineer plans how much compute the company needs, when, and at what cost. They keep services reliable at peak load without…EmergingAgent Reliability EngineerAn Agent Reliability Engineer keeps AI agents dependable in production. They set service targets, watch quality, cost and drift, and make sure…

Where the people come from

SRE, performance engineering, cloud operations or FinOps

Not to be confused with. Monitoring does not prove absence of unobserved harms.

Core skills

  • MLOps and model lifecycle
  • Model evaluation
  • Commercial and financial analysis
  • Reliability and incident response

Where it is used

  • Cross-sector AI platforms
  • Enterprise software and developer tools
  • Semiconductors and compute infrastructure

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