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.
Rashanjot Kaur - AI Architect
Designed agentic RAG pipeline with self-correcting retrieval loops.
6 open role(s)
Applied AI Engineer - Open
Extend agentic RAG, reflection loops, OCR and embedding pipelines, and Pinecone retrieval quality.
Apply →Backend Engineer - Open
FastAPI services, MongoDB, document ingestion, and orchestration for the agentic recommendation stack.
Apply →Frontend Engineer - Open
Build recommendation interface, preference visualization, and clear exploration of multi-dimensional embeddings.
Apply →Data Engineer - Open
OCR output normalization, entity extraction pipelines, and structured data for vector indexing.
Apply →AI Researcher - Open
Benchmark retrieval and preference modeling; study embedding spaces, ranking, and self-reflective agent behavior.
Apply →DevOps / MLOps - Open
Deploy FastAPI/React stack; monitor APIs, Pinecone usage, and pipeline reliability.
Apply →