AI Platform Customization Engineer - Run

Role & responsibilities

• Platform Customization & Operationalization: Design, configure, and extend AI Platform capabilities, service models, workflows, operational policies, escalation paths, runbooks, production controls, and operating procedures for customer applications, infrastructure, and services.
• Observability, Event Intelligence & Operational AI: Customize integrations across metrics, logs, traces, events, alerts, topology, and service health data; implement event correlation, alert prioritization, incident prediction, AI-assisted triage, RCA, remediation recommendations, operational copilots, and agent-driven decision support.
• Workflow Automation & Self-Healing: Build intelligent routing, ticket enrichment, notifications, approvals, escalations, handoffs, closures, automated remediation, runbook automation, and self-healing workflows; establish guardrails, approval controls, risk tiers, rollback mechanisms, and automation effectiveness measures.
• Operational Knowledge & AI Enablement: Configure and optimize runbooks, SOPs, knowledge articles, CMDB data, service maps, architecture documentation, incident history, known-error records, and resolution evidence to power AI Platform agents, copilots, workflows, and AI-driven operations.
• Platform Integrations, Governance & Reliability: Develop integrations with observability, monitoring, cloud, ITSM, collaboration, and automation platforms; implement access controls, auditability, traceability, data governance, human-in-the-loop controls, and governance for AI recommendations and automated actions.
• Optimization & Customer Enablement: Optimize automation coverage, alert-noise reduction, ticket deflection, self-healing success, MTTA, MTTR, incident recurrence, service availability, operational efficiency, and cost outcomes; provide technical guidance, knowledge transfer, platform enhancements, and governed BAU support to customer operations, engineering, SRE, observability, and ITSM teams.