AI Researcher: Entity Resolution & Knowledge Graphs
Auric AI
Mumbai / Bengalore
3-5 years
Today
$22.9K–34.9K/yr
Full-time
Onsite
Skills Required Embeddings
Entity Resolution
Knowledge Graphs
Multilingual NLP
OCR
Hallucination Detection
Graph Databases
Database Theory
Formal Methods
Description Auric AI is hiring an AI Researcher to own entity resolution and knowledge graph challenges where mistaken merges are costly. The role focuses on ambiguous identities across scripts, asymmetrical decision thresholds, extraction from messy data, provenance-protected graph updates, and anomaly discovery.
Company: Auric AI
Role: AI Researcher: Entity Resolution & Knowledge Graphs
Location: Bengaluru/Mumbai (Onsite)
Responsibilities
- Own identity matching across documents and scripts
- Design methods for ambiguous entity resolution with no standard canonical form
- Set thresholds under asymmetric false-match versus missed-match costs with very few labels
- Build extraction approaches for mixed-language reports and OCR'd handwriting
- Create hallucination-resistant entity extraction for assessment-grade output
- Design a self-correcting graph with revisitable merges and full provenance
- Surface structural patterns and anomalies across unrelated entities
- Reason about rare-event ranking where top-of-list precision matters more than aggregate accuracy
Additional Responsibilities
- Work with all-pairs comparison constraints where standard candidate generation fails
- Handle a system where the graph grows with every document and mistakes must be traceable when undone
- Operate on real, messy data where 95% correctness can still be useless if the remaining errors are catastrophic
- This role is not for RAG over a document store
- This role is not for maintaining an ontology designed by someone else
Nice To Have
- Direct experience with entity resolution
- Direct experience with knowledge graphs
- Direct experience with multilingual NLP
More Skills Retrieval, Candidate generation, Phonetic methods, Edit distance, Linguistics, Information retrieval
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