About this opportunity
Tech Mirrors lists this SRE with AI (Don’t share any AI/ML profiles) – C2C jobs opportunity in mountain view, California. Review the employer’s description below for duties, qualifications and application requirements.
Job description
SRE with AI
Location: Mountain View, CA (Onsite)
Contract
Key Responsibilities
Build and extend O11Y pipelines: metrics, distributed tracing, logging, SLO/SLI definitions and dashboards consumed platform-wide.
Instrument AI systems in production: token usage and cost, latency-per-inference, tool-call success rates, response-quality signals, agent loop detection, runaway-cost alerting.
Extend tracing across agentic flows - planner → executor → retrieval → tool calls - spanning GenOS, AI Gateway, and MCP Gateway.
Define SLOs where "available" includes response quality and tool-call success, not just HTTP 200s.
On-call and incident command for AI platform surfaces: model/provider degradation, Bedrock/Gemini failover, semantic-cache issues, prompt-injection events - blameless postmortems with tracked remediations.
Own the SRE side of the progressive-delivery seam:canary analysis, automated rollback decisioning, chaos/resilience testing, blast-radius controls - DevOps builds the pipeline; you define the gates.
Build AIOps agents in the Traffic Agent pattern: intelligent triage, anomaly detection, auto-remediation with hard guardrails.
Drive UX Availability (R30) and Foundations Capabilities (R30); land network, O11Y, and cloud cost savings (tracked YTD KPIs).
Must-Have Qualifications
7+ years SRE/production engineering on Tier-0/Tier-1, high-traffic systems.
Strong Go or Python - this is a build role: tooling, automation, instrumentation.
Hasbuiltobservability stacks, not just consumed them: Prometheus/Grafana, OpenTelemetry, or equivalent at scale, including cardinality and cost control.
Production LLM/ML monitoring: Langfuse, Arize, WhyLabs, or homegrown - token/cost tracking, drift and quality metrics.
Working fluency in AI-system failure modes: nondeterminism, provider limits and outages, context-window overflow, agent loops, cache poisoning.
Kubernetes + AWS operational depth - debugs across cluster, mesh, and gateway layers.
Structured incident-command and postmortem experience.
Nice-to-Have
AIOps / LLM-applied-to-ops: auto-triage, incident summarization, remediation agents.
Chaos engineering (Litmus, Gremlin, or homegrown); eBPF or deep network debugging.
FinOps / cost engineering; fintech or regulated-industry reliability experience.
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Worksite address
mountain view, CA, 94039, US
Who can apply
Review the original listing for work authorization, qualifications and employer requirements.