AI​/ML Data Graph Engineer

Sapience AI is the collective intelligence platform for professional communities. We sit above the CRMs, AMS platforms, and knowledge bases that organizations already run, and we turn the expertise scattered across them into something every member can search, act on, and share.

The intelligence a community needs is already inside it. Most organizations just cannot reach it. Knowledge lives in silos, in legacy systems, in the heads of a few experts, and in fragmented records no one can connect. We change that.

Our work is grounded in four commitments: technology elevates people and never replaces them, the best expertise is already inside the community, everything is built on trust, and every deployment is purpose-driven for the organization it serves.

Let’s achieve more, together.

This role builds the structured knowledge that collective intelligence reasons over. You own the KO (knowledge object) graph: the layer that turns a community’s scattered expertise into connected, queryable knowledge the COGENT architecture can use.

You work where messy real-world data becomes trustworthy structure: ingesting, resolving, connecting, and modeling knowledge so reasoning has something solid to stand on.

You partner closely with neuro-symbolic AI and applied AI, and you are the reason the platform can answer questions that span a community’s knowledge instead of isolated documents.

Language models are fluent, but fluency is not knowledge. To reason over a community’s expertise with rigor, the platform needs that expertise structured, connected, and trustworthy, not just retrieved as text.

Building a knowledge graph from real, fragmented sources is hard: entities to resolve, relationships to infer, quality to enforce, and provenance to preserve. The graph is only as good as the engineering behind it.

The AI/ML Data and KO Graph Engineer builds that foundation. You turn scattered knowledge into a graph the COGENT architecture can reason over, so members get answers grounded in their community’s real expertise.

You hold seven areas of responsibility across the knowledge layer. Each one is yours to set direction on, build, and measure.

• 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.

• Measure the quality, coverage, and freshness of the graph against what…