- Own and evolve the hybrid search pipeline – lexical retrieval, dense vector search, reciprocal rank fusion, and multi-stage reranking on Elasticsearch
- Build and train adaptive retrieval models – lightweight classifiers and ranking models that configure search behavior per query, per category, per context
- Design and productionize the learning-to-rank system – from feature engineering through model training to production deployment and A/B evaluation
- Build the search feedback loop – instrument and integrate behavioral signals into ranking and retrieval
- Build the business and ordering layer – separating organic relevance from sponsored/partner placement with quality gates, slot allocation, campaign configuration, and an auction-style approach
- Own the offline enrichment pipeline – LLM-based product enrichment at scale, data quality monitoring, and index management
- Instrument and evaluate everything – bulk evaluation pipelines, per-category metric tracking, regression detection, experiment analysis
- Integrate query understanding outputs into retrieval – translating extracted attributes, intents, and constraints into filters, boosts, and retrieval strategy decisions
PythonElasticSearchLLM