Site Reliability Engineer - Observability
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About the Role
We are hiring an SRE focused on observability, automation, and runtime reliability for AI platforms and internal agentic systems. This is not a generic SOC role. It is an engine.
Key Skills for This Role
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Overview
- We are hiring an SRE focused on observability, automation, and runtime reliability for AI platforms and internal agentic systems.
- This is not a generic SOC role.
- It is an engineering role for someone who builds telemetry, automates findings-to-fix loops, improves production readiness, and keeps AI systems measurable, resilient, and controllable in production.
- Tech stack
- Python for automation and workflow integration
- Observability tooling: metrics, logs, traces, OpenTelemetry, Datadog or adjacent stacks
- AWS logging, telemetry, IAM-aware diagnostics, and infrastructure scripting
- CI/CD integration for runtime checks, rollback drills, and policy validation
- Nice to have: Wiz, CrowdStrike, Orca, GuardDuty, WAF / RASP-style controls, MCP / agent telemetry
Responsibilities
- Design and operate the telemetry and observability layer for AI platforms, including audit trails, tool-call logs, correlation IDs, traces, and runtime visibility across service boundaries.
- Build automated findings-to-fix loops for AI and cloud platforms, integrating signals from tooling such as Wiz, Astrix, or future AI security products into pragmatic remediation workflows.
- Implement reliability and hardening controls for internal AI systems, including alerting, health checks, rollback drills, kill-switch validation, rate limiting, and drift detection.
- Codify detections, policies, and operational checks as code where they reduce toil, prevent regressions, and improve platform control.
- Review platform and AI-application changes from a reliability and application-hardening perspective, especially around secrets, telemetry, external calls, risky MCP usage, and production readiness.
- Own AI-platform-specific operational readiness and partner with central IT / EAS / SOC teams for escalations, handoffs, and shared incident workflows when needed.
- Continuously improve production readiness through automation, post-incident learning, and repeatable playbooks for AI runtime issues.
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