Agentic Infrastructure Observability Engineer

Responsibilities

• Design and implement end-to-end observability pipelines covering metrics, logs, traces, and cost telemetry for agentic systems.
• Build dashboards and alerting systems to monitor reliability, performance, and drift in real-time.
• Track LLM usage, context windows, token allocation, and multi-agent interactions.
• Build monitoring hooks into LangChain, LangGraph, MCP, and RAG pipelines.
• Define and monitor SLOs, SLIs, and SLAs for agentic workflows and inference infrastructure.
• Conduct root cause analysis of agent failures, latency issues, and cost spikes.
• Integrate observability into CI/CD and AgentOps pipelines.
• Develop custom plugins and scripts to extend observability for LLMs and data pipelines.

Requirements

• 3–6 years of experience in SRE, DevOps, or Observability Engineering.
• Strong knowledge of observability tools such as Prometheus, Grafana, ELK, OpenTelemetry, and Jaeger.
• Experience with cloud-native infrastructure (AWS, GCP, or Azure) and Kubernetes monitoring.
• Proficiency in Python, Go, or Bash for scripting and automation.
• Understanding of AI/LLM pipelines, RAG systems, and vector databases.
• Hands-on experience with CI/CD pipelines and monitoring-as-code.

Preferred Qualifications

• Experience with AgentOps tools like LangSmith, PromptLayer, Arize AI, or Weights & Biases.
• Exposure to AI-specific observability including token usage, model latency, and hallucination tracking.
• Knowledge of Responsible AI monitoring frameworks.
• Background working in regulated industries such as BFSI or GRC.

About the Company

Xenonstack is a fast-growing Data and AI Foundry for Agentic Systems. We enable enterprises to gain real-time and intelligent business insights through our specialized platforms for AI Agents, Vision AI, and Inference AI Infrastructure. Our mission is to accelerate the transition to AI + Human Intelligence by building platforms that are scalable, reliable, and observable by design.