Graph AI
Heterogeneous graph neural network for preference-aware entity matching. GNN message-passing over multi-relational interaction graphs with hybrid ranking. Cold-start mitigation across sparse interaction regimes.
Heterogeneous graph neural network for preference-aware entity matching. GNN message-passing learns preference embeddings through neighborhood aggregation. Hybrid ranking combines structural scores with content-based features.
Traditional recommendation relies on aggregate signals. Collaborative filtering suffers from cold-start and data sparsity. The interaction graph contains rich preference signals that flat models ignore.
Heterogeneous graph construction with representation fallback. GNN message-passing learns preference embeddings. Hybrid ranking weighted dynamically based on interaction density. Content-based fallback for cold-start.
Rashanjot Kaur - ML Architect
Designed heterogeneous GNN architecture and cold-start mitigation strategy.
6 open role(s)
Applied AI Engineer - Open
Train and refine PyTorch Geometric models, hybrid ranking, and collaborative filtering integration.
Apply →Backend Engineer - Open
Build graph construction pipeline, Flask APIs, PostgreSQL/Redis serving, and inference integration.
Apply →Frontend Engineer - Open
React UI for graph exploration, rankings, and cold-start explainability.
Apply →Data Engineer - Open
Graph construction from interaction logs, feature stores in PostgreSQL, and data quality for GNN inputs.
Apply →AI Researcher - Open
GNN and cold-start experiments, offline evaluation methodology, and hybrid ranking studies.
Apply →DevOps / MLOps - Open
Training and deployment pipelines, Redis caching, monitoring for inference and batch graph builds.
Apply →