Evidence Retrieval AI
Multi-agent evidence retrieval system with supervisor-routed specialist delegation. Multi-stage retrieval pipeline across hybrid vector search, LLM-scored relevance ranking, and quality-weighted synthesis with inline evidence metadata. Retraction-filtered retrieval with citation traversal and consensus detection across specialist outputs.
Multi-agent evidence retrieval system with domain-specialized agents. A supervisor routes queries by LLM reasoning to relevant specialists; each runs a multi-stage pipeline — hybrid vector search, external API fallback, retraction filtering, LLM reranking, and quality-weighted sort. Evidence quality metadata propagates inline; consensus detection identifies agreement, debate, and emerging findings across specialist outputs.
Evidence synthesis across heterogeneous domain-specific corpora requires concurrent retrieval from multiple specialized sources, quality assessment at retrieval time, and attribution-preserving synthesis. Single-model retrieval flattens domain distinction and lacks the multi-source aggregation needed to surface contradictions.
Supervisor-mediated specialist delegation routes each query by LLM reasoning. Each specialist runs a multi-stage retrieval pipeline with hybrid semantic search, external API calls with local databases, citation traversal, and LLM-based relevance scoring that drops low-quality chunks before synthesis. Consensus detection operates across specialist outputs to surface agreement, debate, and emerging findings.
Applied AI Engineer
Build and extend RAG evaluation pipelines with self-consistency verification. Optimize retrieval accuracy for medical literature and clinical guidelines.
Apply →Backend Engineer
Design search infrastructure, document ingestion APIs, and caching layers. Build scalable query processing with async architecture.
Apply →Frontend Engineer
Build the clinician-facing search interface — result rendering, source highlighting, confidence indicators. Focus on speed and clarity for time-pressed users.
Apply →Data Engineer
Parse PDFs, medical records, and research papers into structured searchable formats. Build extraction pipelines for tables, citations, and clinical metadata.
Apply →AI Researcher
Evaluate retrieval quality across medical domains. Design benchmark datasets, run ablation studies, and research domain-specific embedding and ranking strategies. Contribute to evaluation methodology publications.
Apply →DevOps / MLOps
Cloud deployment, latency optimization, search index management. Monitor retrieval quality in production.
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