AI Reliability & Observability Platform
- Built a trace-based observability system for LLM applications where traditional infrastructure metrics (CPU, memory, uptime) were insufficient — failures originated from inference latency, prompt execution, and orchestration logic.
- Developed evaluation pipelines and AI guardrails to enforce reliability standards across model behavior, addressing the fundamental challenge that AI systems generate probabilistic failures not caught by standard alerting.
- Implemented inference monitoring and AI runtime observability dashboards, enabling production-grade debugging of LLM workflows that previously had no operational visibility layer.