Note: The job is a remote job and is open to candidates in USA. Sapience AI is a collective intelligence platform that turns expertise scattered across CRMs, association management systems, and knowledge bases into searchable, actionable, and shareable knowledge. The AI/ML Data Knowledge Graph Engineer will build and operate the knowledge object graph, including schemas, ingestion pipelines, entity resolution, provenance, quality measurement, and graph serving for reasoning and retrieval systems.
Responsibilities
- Build and maintain the KO graph that structures a community’s knowledge for reasoning
- Design schemas, ontologies, and relationships that reflect how expertise actually connects
- Make the graph queryable, performant, and reliable at scale
- Build pipelines that extract knowledge from documents, systems, and community sources into the graph
- Turn unstructured and semi-structured content into structured knowledge objects
- Keep the graph current as a community’s knowledge changes
- Resolve entities, deduplicate, and connect knowledge across fragmented sources
- Enforce quality so members can trust what the graph tells them
- Detect and handle conflicts and gaps in the knowledge
- Preserve provenance so every piece of knowledge can be traced to its source
- Build the structure that lets the platform show its work and earn member trust
- Protect sensitive community knowledge with correct access and governance
- Partner with neuro-symbolic and applied AI to serve the graph into reasoning and retrieval
- Shape the graph so it supports both symbolic reasoning and neural retrieval
- Make knowledge access fast enough for production answers
- Build the data pipelines and platform the knowledge layer depends on
- Instrument the pipelines so quality and freshness can be measured
- Turn recurring ingestion needs into reusable connectors
- Measure the quality, coverage, and freshness of the graph against what communities need
- Build the evaluation that tells whether the knowledge layer is improving
- Use evidence to steer where to invest next
Skills
- Five or more years in data engineering, knowledge graph engineering, or a related field
- Hands-on experience building and operating knowledge graphs or graph databases
- Strong data pipeline engineering, including ingestion and transformation
- Experience with entity resolution, deduplication, and data quality
- Solid grounding in knowledge representation, ontologies, or schema design
- Strong Python and SQL, plus graph query languages
- Care for provenance, trust, and protection of sensitive data
- Experience serving graphs into retrieval or reasoning systems
- Familiarity with neuro-symbolic AI and how structure supports reasoning
- Experience with embeddings, vector search, and hybrid retrieval
- Experience integrating CRM, AMS, or knowledge-base sources
- Domain understanding of knowledge-intensive or professional communities
Benefits
- Early stage equity
- Generous health and wellness benefits
Company Overview