· CV-Driven Inspection: Develop and maintain Computer Vision models for real-time automated defect recognition on the production line.
· QDN Automation: Architect an automated QDN Lot Containment system that triggers immediate holds or re-routing of suspected sub-standard lots based on AI-detected anomalies.
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Test the problem against the model in batches of parallel attempts, tuning the problem difficulty until the agent only succeeds in a small number of attempts.
Once you're happy with the task, and it scores within range, the task goes to a senior reviewer in your subfield. They will provide feedback to ensure task quality is high.
Multi-Agent Systems: Develop collaborative multi-agent systems for fleet operations, maintenance intelligence, route planning, and customer support automation.
Possess a solid understanding of business information and documents, including but not limited to financial reports, inventory records, and production schedules.
Leveraging BI tools to extract actionable insights from data. Ensure the accuracy and integrity of information within the ERP system, enabling informed decision-making at all levels.
Develop and implement system logic enhancements to support evolving business requirements.
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Develop and implement machine learning models (regression, classification, clustering) to predict maintenance needs and optimize operations.
Leverage Large Language Models (LLMs) to enhance data products through automated feature extraction, data enrichment, and intelligent information retrieval and decision making.
Create scalable data solutions that can handle real-time aircraft sensor data and maintenance logs.
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