Staff Software Engineer , AI Advice Centre
Royal Bank of Canada Toronto, ON
- Own the intent detection and classification models for the bank's call-centre platform: labelling, training, and inference over live call transcripts from AWS Transcribe, across thousands of daily banking calls.
- Built an LLM ensemble labelling pipeline (GPT-4o mini, GPT-4.1 mini, GPT-5 mini) with consensus voting and escalation to larger thinking models for tie-breaks, to generate training labels for fine-tuning.
- Ran a knowledge distillation workflow using the ensemble as teacher to fine-tune Arctic Embed 2.0 Large into a domain student model for banking intent semantics.
- Built the continuous training pipeline on S3 and Apache Airflow: dataset ingestion, retraining, evaluation, and deployment to OpenShift.
- Benchmarked SVM (RBF), logistic regression, SetFit (head and full fine-tune), centroid, and fine-tuned Arctic Embed classifiers for production intent classification.
- Served Arctic Embed 2.0 Large with ONNX Runtime on CPU-only OpenShift, tuned with AVX-512 and related flags (no VNNI), inside a Spring Boot service on Java 25 that handles thousands of concurrent advisor and client conversations in real time.
- Trained a class-balanced logistic-regression head on the ensemble-labelled data to classify caller intent live as each conversation progresses, reaching up to 85% accuracy.