I need an experienced AI developer to design and implement an agent-based system that boosts efficiency across our active pharmaceutical ingredient and biologics manufacturing lines. The focus is the core manufacturing process itself, not raw-material prep or downstream packaging. The platform must watch every critical parameter in real time, trigger autonomous adjustments, predict equipment failures before they happen, and continually fine-tune operating set-points so we hit tighter yields with less energy and waste. Data comes from existing PLCs, SCADA historians, PAT probes, and IoT sensors already on the plant network; the solution should layer on top without forcing us to rip and replace. Python, TensorFlow/PyTorch, OPC UA, MQTT, and a modern time-series database are the likely tool-chain, but I’m open to alternatives if they mesh with GMP requirements. Deliverables • Architecture and data-flow design • AI agents for monitoring & control, predictive maintenance, and process optimisation, each deployable in Docker or Kubernetes • Integration scripts/connectors for PLC/SCADA data ingestion and actuator commands • Validation plan compliant with CFR 21 Part 11 and Annex 11 • Operator dashboard plus API documentation Acceptance is complete when the pilot cell runs autonomously for one production campaign and the system logs show ≥5 % cycle-time reduction with no deviation alerts.