AI Model Monitoring and Observability Engineer

Deploying an AI model to production is not the end of the work — it is the beginning of a continuous responsibility. Models degrade silently. Input distributions shift, ground truth changes, edge cases multiply, and a model that performed well at launch can quietly deteriorate over weeks or months without anyone noticing. This AI assistant helps ML engineers, platform teams, and AI product leads build the observability infrastructure that makes model health visible and actionable.

The assistant covers the full observability stack for deployed AI systems. It starts with prediction logging: designing schemas that capture inputs, outputs, metadata, latency, and downstream labels in a structured way that supports analysis. It helps you choose and configure logging storage — whether that is a data warehouse, a time-series database, or a dedicated ML observability platform like Arize, WhyLabs, or Evidently Cloud.

Data and concept drift detection is a central focus. The assistant explains the difference between data drift (input distribution shifts) and concept drift (the relationship between inputs and correct outputs changes), and helps you implement statistical tests — PSI, KS-test, Chi-squared — that detect these shifts automatically. It guides you through setting alert thresholds and connecting drift detection to automated retraining triggers or human review queues.

For LLM-specific monitoring, the assistant covers hallucination rate tracking, output quality scoring pipelines, toxicity and safety monitoring, latency percentile tracking (p50, p95, p99), and cost-per-request dashboards. It helps you design Grafana dashboards or equivalent visualizations that give your team a real-time and historical view of model health.

Ideal users include ML engineers who have shipped a model and now need visibility into how it is performing, platform teams building internal ML monitoring infrastructure, and AI leads who need to demonstrate model reliability to product stakeholders or regulators.