Senior AI Observability engineer
Tasks
- Automate incident summarization and root cause hypotheses
- Automate safe remediation with approval gates
- Build LLM agent workflows
- Cluster events and incidents
- Correlate and deduplicate alerts
- Create retrieval knowledge pipelines
- Develop AIOps anomaly detection
- Draft post mortems from telemetry
- Forecast capacity and failures
- Generate blast radius analysis
- Implement AI safety and governance guardrails
- Index and evaluate vector retrieval quality
- Instrument AI services with OpenTelemetry GenAI tracing
- Monitor model SLOs cost latency and quality
- Run LLMOps and MLOps pipelines
Perks/Benefits
- N/A
Skills/Tech-stack
A/B | A/B Testing | AWS | Agent Orchestration | Alert Correlation | Anomaly Detection | Autogen | Azure | B testing | BGP | CI/CD | CMDB | Cloud network | Cost observability | DNS | Dynamic Baseline | EVPN | Embeddings | Event Clustering | Firewall | Function Calling | Google Cloud | Guardrails | Inference Logging | Injection Defense | LLM | LLMOps | Langgraph | Latency Observability | Load Balancer | MLOps | MPLS | Machine Learning | Model Context Protocol | Model versioning | OSPF | Offline evaluation | Online evaluation | OpenTelemetry | OpenTelemetry GenAI | OpenTelemetry GenAI Tracing | PII Redaction | Predictive forecasting | Private Cloud | Prompt engineering | Prompt injection | Prompt injection defense | Retrieval-Augmented Generation | SD-WAN | Semantic Kernel | ServiceNow | Shadow testing | Storage | TCP/IP | Time Series | Token Cost | Token Cost Observability | Tool-Calling | Transit Routing | VLAN | VPC | VXLAN | Vector Database | Virtualization
Education
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