Recommendation AI
Agentic RAG pipeline with self-reflective retrieval and multi-dimensional embedding space for preference modeling. Unstructured document ingestion via OCR with domain-specific entity extraction.
Agentic RAG system with self-reflective retrieval across multi-dimensional embedding space. OCR and entity extraction produce structured representations from unstructured inputs. Self-correcting agent behavior through reflection loops.
Unstructured documents present information in inconsistent formats. Existing recommendation approaches use aggregate signals that cannot model individual preferences at granular levels.
Multi-stage agentic pipeline. OCR and entity extraction produce structured representations. Custom multi-dimensional embedding space. Self-reflective agent evaluates candidate outputs through reflection loops.
Applied AI Engineer
Extend agentic RAG, reflection loops, OCR and embedding pipelines, and Pinecone retrieval quality.
Apply →Backend Engineer
FastAPI services, MongoDB, document ingestion, and orchestration for the agentic recommendation stack.
Apply →Frontend Engineer
Build recommendation interface, preference visualization, and clear exploration of multi-dimensional embeddings.
Apply →Data Engineer
OCR output normalization, entity extraction pipelines, and structured data for vector indexing.
Apply →AI Researcher
Benchmark retrieval and preference modeling; study embedding spaces, ranking, and self-reflective agent behavior.
Apply →DevOps / MLOps
Deploy FastAPI/React stack; monitor APIs, Pinecone usage, and pipeline reliability.
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