AI/ML Data Knowledge Graph Engineer

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

Requirements:

• 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