AI Automation Monitoring & Maintenance

I run several AI-driven services that rely on structured data stored in relational databases, and I want to keep them healthy, fast, and dependable. The most immediate need is continuous system monitoring and preventive maintenance: watching model performance, data pipelines, resource usage, and alerting thresholds so issues are caught long before they affect users. Alongside that oversight, I’m keen to squeeze more efficiency out of the algorithms already in production. Query plans, feature-engineering code, batch jobs, and model inference routines all hold room for improvement, and every millisecond we save lets us serve more users on the same hardware. Here’s what I would like delivered: • A baseline health audit of the current AI stack, highlighting failure points and noisy alerts • A monitoring dashboard (Elevenlabs) wired to real-time metrics, with clear alert rules and SLAs documented • A short, actionable report outlining algorithm-level optimizations—SQL refactors, model pruning, smarter caching, vectorized operations, etc.—plus measured before/after results • A monthly maintenance log template so future work and incident responses are tracked consistently I am looking for someone to take on the overall maintenance, monitoring and health of the the AI & Automation system in place which can vary from client to client. That person also needs to be suffecient in making changes, fixing bugs, optimisation and implementing new features up request.