Day-to-day owner of the AI cost-and-reliability surface: tracking token spend, latency budgets, eval drift, and vendor contract performance across a sprawling i
An AI Operations Manager runs the day-to-day machinery of deployed AI: monitoring agent fleets and automation quality, managing escalation and exception queues, coordinating model/prompt updates with stakeholders, and owning the operational metrics (accuracy, cost, latency, deflection) that leadership reads. If AI engineers build the systems, AI ops keeps them honest in production.
The 2026 role emerged because agentic automation created a new operational layer: autonomous processes that mostly work, sometimes fail oddly, and always need supervision, audit trails, and continuous tuning. Strong AI ops managers blend process discipline with enough technical fluency to triage failures and direct fixes.
Monitoring automation quality dashboards, triaging exceptions agents couldn't handle, coordinating prompt or model changes with engineering, reporting cost and accuracy metrics, and maintaining audit trails: the standing supervision layer every deployed AI fleet needs.
Roughly $100k–$170k base in the US, rising with the criticality of the automations managed: revenue-touching and regulated processes pay top of band.
Semi-technical: you won't write production code, but you must read traces, understand failure modes, interpret eval metrics, and direct engineering fixes credibly. Process and stakeholder skills carry equal weight.
Volunteer to own a deployed automation: build its exception queue, metrics, and playbook. One quarter of documented production supervision converts ops experience into AI-ops credibility.