Api Error Handling vs Senior Ml Engineer for Observability & Monitoring

Implements standardized API error responses with proper status codes, logging, and user-friendly messages. Use when building production APIs, implementing error recovery patterns, or integrating error monitoring services.

ML engineering skill for productionizing models, building MLOps pipelines, and integrating LLMs. Covers model deployment, feature stores, drift monitoring, RAG systems, and cost optimization. Use when the user asks about deploying ML models to production, setting up MLOps infras…