Recovery and Incident Manager with AI-Ops
Tasks
- Build AI powered operational agents
- Conduct root cause analysis
- Create corrective action plans
- Create self-healing workflows
- Define and monitor SLAs SLOs and operational KPIs
- Develop ITIL aligned incident problem change and event management
- Develop reliability and availability strategies
- Document incident response activities
- Enable predictive monitoring and anomaly detection
- Implement AIOps for incident detection and remediation
- Implement SRE practices
- Improve mean time to detect and mean time to resolve
- Lead enterprise monitoring initiatives
- Lead incident management
- Mentor operational teams and drive automation culture
- Monitor and analyze major incidents
- Partner with infrastructure and DevOps teams
- Use ServiceNow workflow automation
Perks/Benefits
- N/A
Skills/Tech-stack
AIOps | AWS | Anomaly Detection | Artificial Intelligence | Autonomous operations | Azure | Cause analysis | Change Management | Cloud Operations | Cloud platform | DevOps | Dynatrace | Event management | Google Cloud | Google Cloud Platform | IT Service Management | IT service | ITIL | Incident Management | Incident Response | Infrastructure Operations | Learning operations | Machine Learning | Machine Learning Operations | Mean Time To Detect | Mean Time to Resolve | Monitoring | Network Monitoring | Network Operations Center | Network operations | Observability | Operations Center | Predictive Monitoring | Problem Management | Reliability Engineering | Root Cause Analysis | Root cause | Service Level | Service Level Agreements | Service Management | Service-Level Objectives | ServiceNow | Site Reliability | Site Reliability Engineering | Time To Resolve | Time to Detect | Workflow automation
Regions
Countries
States
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