Developed an end-to-end MVP counseling platform named AImpathy, integrating multi-modal deep learning pipelines to provide actionable insights from therapy sessions.

• Designed a scalable video ingestion pipeline using AWS S3 for storage and AWS SQS for event-driven processing — enabling real-time data flow.
• Engineered a Transformer-based multi-modal system that combines Whisper ASR for speech transcription and custom models for emotional analysis of audio and text.
• Containerized machine learning models using Docker and deployed them on AWS ECS for scalable and reliable performance.
• Leveraged OpenCV for preprocessing visual cues from recorded sessions.

The project aimed to assist therapists by automating documentation and providing emotion analytics to improve session outcomes.