Roles and Responsibilities

• Architect scalable reliability solutions leveraging best-in-class practices in AI Ops and historical analytics.
• Design and implement Historical Analytics Architecture for post-incident analysis, system health insights, and long-term trend discovery.
• Lead the strategy and implementation of Data Fabric Architecture to unify data access and management across hybrid and multi-cloud environments.
• Drive AI Ops initiatives, integrating machine learning and automation into incident detection, root cause analysis, and system optimization.
• Develop and execute an AI Observability Strategy to enable intelligent insights, predictive alerts, and self-healing systems.
• Champion OpenTelemetry (OTel) adoption and architecture, ensuring standardized and vendor-neutral observability instrumentation across the organization.
• Provide architectural oversight on high-availability systems, reliability KPIs (e.g., SLOs, SLIs), and risk assessments.
• Collaborate cross-functionally with development, operations, product, and executive teams to align reliability architecture with business goals.
• Mentor engineering teams on best practices in reliability engineering and observability.

Required Qualifications

• 8+ years of experience in site reliability, systems architecture, or a related field.
• Proven experience architecting Historical Analytics systems and data pipelines at scale.
• Deep understanding of Data Fabric concepts, including data virtualization, governance, and real-time access layers.
• Hands-on experience developing or leading AI Ops and AI Observability strategies within production environments.
• Strong knowledge of OpenTelemetry (OTel) standards, best practices, and implementation patterns.
• Proficiency in cloud-native architectures (AWS, Azure, GCP), containerization (Kubernetes, Docker), and infrastructure as code (Terraform, etc.).
• Solid understanding of monitoring tools (e.g., Prometheus, Grafana, Splunk, Datadog, New Relic) and logging platforms.
• Excellent communication and stakeholder management skills.

Preferred Qualifications

• Master’s degree in Computer Science, Engineering, or related field.
• Experience with MLOps, event-driven architecture, and real-time data processing.
• Familiarity with security and compliance in large-scale distributed systems.
• Contributions to open-source observability or reliability projects.

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