[Hiring] AI/ML Data Knowledge Graph Engineer @Sapience AI Corporation

Role Description

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.

Areas of Responsibility

• Knowledge graph engineering:
  • 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.
• Ingestion and knowledge extraction:
  • 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.
• Entity resolution and quality:
  • 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.
• Provenance and trust:
  • 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.
• Serving knowledge to COGENT:
  • 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.
• Data pipelines and platform:
  • 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.
• Evaluation of knowledge quality:
  • 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.

Required Qualifications

• 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.

Preferred Qualifications

• 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.

Skills & Competencies

• Knowledge graph and ontology engineering.
• Ingestion, extraction, and transformation pipelines.
• Entity resolution, deduplication, and data quality.
• Provenance, governance, and protection of sensitive knowledge.
• Serving graphs into retrieval and reasoning.
• Evaluation of knowledge quality and coverage.
• Turning recurring ingestion into reusable capability.

Services & Tools Experience

• Graph databases (for example Neo4j-class systems) and graph query languages (Cypher, SPARQL, or GQL).
• Data pipeline and orchestration tools.
• Entity resolution and data-quality tooling.
• Vector databases and embedding models for hybrid retrieval.
• Python and SQL as primary languages.
• Cloud data platforms and storage.

Compensation

• Base Salary: $204,000 - $216,000 + early stage equity.
• Generous health and wellness benefits.