AI Electronic Health Record Specialist vs AI Emotion Detection Specialist

B AI Emotion Detection Specialist Only

• Affective computing fundamentals and emotion taxonomy design (Ekman, Plutchik, dimensional PAD models)
• Natural Language Processing for sentiment and emotion classification (transformers, fine-tuning BERT/GPT variants)
• Multimodal signal processing - text, speech prosody, facial action units (FACS), and physiological data
• Dataset curation and emotion annotation workflows including inter-annotator agreement measurement
• Deep learning model training, hyperparameter optimization, and transfer learning with PyTorch / TensorFlow
• Real-time inference pipeline design using streaming architectures (Kafka, WebSockets, gRPC)
• Bias auditing and fairness evaluation across demographics, cultures, and emotional expression styles
• MLOps practices - model versioning, A/B testing, monitoring for drift in emotion distributions