Portfolio projects built around retrieval, agents, and multimodal ML.

Production-style semantic retrieval system with LLM agent orchestration for natural-language movie search.

CineSeek is a production-style retrieval system combining dense embeddings, FAISS ANN indexing, and an LLM-based agent for query rewriting, reranking, and result explanation.

The agent is integrated into the retrieval pipeline to improve robustness for complex natural-language queries, enabling better handling of ambiguity, intent refinement, and long-tail search scenarios under latency constraints.

Lightweight retrieval adaptation project for improving a strong frozen sentence-transformer baseline under a realistic small-data setting.

Compares linear adapters, residual MLP adapters, and concat-based item fusion under full-catalog contrastive training, with evaluation across recall, MRR, NDCG, parameter count, and retrieval latency.

The main finding is that preserving title and overview as structured item-side signals can outperform deeper nonlinear adapters, suggesting that representation structure matters more than extra capacity in this setting.

CLIP-based multimodal retrieval system for text, poster, and hybrid movie search.

Implements a CLIP-based dual-encoder pipeline using Vision Transformer (ViT) image features and text embeddings, with FAISS indexing and offline evaluation.

The project analyzes modality fusion strategies, retrieval behavior, and quality/latency tradeoffs across text-only, image-only, and hybrid queries in a CLIP-style embedding space.