AI/ML Data Knowledge Graph Engineer
About The Position
Sapience AI is seeking an AI/ML Data Knowledge Graph Engineer to build the structured knowledge that collective intelligence reasons over. This role owns the KO (knowledge object) graph, the layer that transforms a community’s scattered expertise into connected, queryable knowledge for the COGENT architecture. You will work at the intersection of messy real-world data and trustworthy structure, ingesting, resolving, connecting, and modeling knowledge. This role is crucial because language models are fluent but lack true knowledge; structuring, connecting, and ensuring the trustworthiness of expertise is essential for rigorous reasoning over a community’s knowledge. The AI/ML Data and KO Graph Engineer will build this foundational layer, turning fragmented knowledge into a graph that the COGENT architecture can reason over, ensuring members receive answers grounded in their community’s expertise.
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.
Nice To Haves
- 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.
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.
Benefits
- Generous health and wellness benefits
Stand Out From the Crowd
Upload your resume and get instant feedback on how well it matches this job.
What This Job Offers
Job Type
Full-time
Career Level
Senior
Education Level
No Education Listed
