Prinicipal Engineer,Site Reliability

Responsibilities:

• Lead resolution of high-severity/complex incidents across hybrid infrastructure.
• Architect and implement automation frameworks, self-healing workflows, and AI-driven ops.
• Define SRE best practices, reliability SLIs/SLOs/SLAs, and operational standards.
• Partner with application and platform engineering teams to improve resilience.
• Drive observability maturity: predictive monitoring, anomaly detection, automated RCA.
• Own continuous improvement of Engineer(s)/Sr Engineer(s) runbooks and automation pipelines.
• Provide technical leadership, mentor junior SREs, and conduct training.
• Identify new technologies, tools, and processes that elevate operational excellence.

Skills:

Mandatory Skills (Must-Have):

Incident Command & Complex Troubleshooting:

• Expectation: Take leadership during high-severity outages, orchestrating technical response across teams.
• Example: Lead a Sev-1 bridge call where multiple microservices are failing due to cascading Kubernetes issues; coordinate DB, infra, network, security and app teams to isolate the problem.

Deep Kubernetes & Distributed Systems Expertise:

• Expectation: Design, troubleshoot, and optimize complex Kubernetes clusters and multi-region deployments
• Example: Diagnose why inter-cluster communication in a service mesh is causing intermittent API failures and propose architectural fixes.

Automation Framework Design (Infra & Ops):

• Expectation: Architect automation platforms to reduce manual toil, enable self-service, and support auto-remediation.
• Example: Build an Ansible/Terraform-based automation pipeline that provisions, configures, and tests new app environments with zero manual steps.

Observability Strategy & Advanced Monitoring:

• Expectation: Define enterprise-wide observability standards (SLIs/SLOs/SLAs), implement anomaly detection, and predictive monitoring.
• Example: Roll out a metrics-based SLO framework for all API services with automated burn-rate alerts in Prometheus.

Database & Application Performance Engineering:

• Expectation: Tune databases, caching layers, and app performance to handle scale.
• Example: Identify DB query patterns that degrade API performance and recommend schema/index optimizations.

Cross-Domain SME Knowledge (Networking, Storage, APIs):

• Expectation: Act as a go-to expert across infrastructure layers.
• Example: Troubleshoot why API gateway latency spikes correlate with storage backend bottlenecks.

AI/ML in Operations (AIOps):

• Expectation: Integrate AI-driven platforms for anomaly detection, auto-remediation, and incident prediction
• Example: Deploy an ML model that predicts storage saturation 24 hours before impact, triggering automated cleanup.

Mentorship & Technical Leadership:

• Expectation: Act as SME, guiding Engineer(s)/ Sr Engineer(s), creating playbooks, and driving operational excellence.
• Example: Conduct deep-dive training sessions on advanced Kubernetes troubleshooting for Sr Engineer(s).

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