Role Summary
The Cloud AIOps Architect defines and implements AI-driven operational capabilities across cloud and hybrid environments. The role combines cloud architecture, observability, reliability engineering, automation and artificial intelligence to transform operations from reactive support models to predictive and increasingly autonomous operations. While providing architectural leadership, the role remains hands-on, actively contributing to solution design, prototyping, automation development, operational analytics and complex incident investigations.
Key Responsibilities
- Define the enterprise AIOps vision, roadmap and architecture.
- Design solutions for anomaly detection, event correlation, predictive analytics and root cause analysis.
- Implement AI-powered incident diagnosis, operational intelligence and knowledge-assistant platforms.
- Develop enterprise observability strategies across infrastructure, networks, platforms and applications.
- Architect automation, self-healing and event-driven remediation capabilities.
- Partner with SRE, Operations and Engineering teams to improve reliability, availability and operational maturity.
- Provide architectural leadership for cloud infrastructure, networking and multi-cloud operations.
- Mentor engineers and architects while contributing directly to technical implementations.
Qualifications & Experience
- 10+ years of experience in cloud infrastructure, networking, platform engineering, operations or architecture.
- Proven experience implementing observability, automation or AIOps solutions.
- Experience leading reliability, operational excellence or cloud transformation initiatives.
- Demonstrated ability and desire to work hands-on with engineering teams, prototypes, automation and operational tooling.
Technical Skills
- Cloud Platforms: Azure, AWS and/or GCP
- Observability: Grafana, Prometheus, Datadog, Dynatrace, Splunk, OpenTelemetry
- AIOps & AI: Generative AI, Agentic AI, RAG, anomaly detection, predictive analytics, operational copilots
- Automation: Python, PowerShell, REST APIs, Terraform, Bicep, CloudFormation
- Reliability Engineering: SRE, incident management, service reliability and operational analytics
Preferred Qualifications
- Experience building internal AIOps platforms and AI-powered operational tooling.
- Experience integrating machine learning models into production operational workflows.
- Strong cloud networking and distributed systems background.
- Experience developing automation frameworks that reduce operational effort and improve service reliability.
Success Measures
- Reduced operational incidents and service disruptions.
- Improved availability, reliability and customer experience.
- Reduced MTTD and MTTR through AI-driven operations.
- Increased automation and self-healing adoption.
