Lead AI Knowledge Architect

Pearson

City of Albany (NY)

On-site

USD 140,000 - 160,000

Full time

41 hours ago

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Job summary

Pearson is seeking a Lead Specialist, AI Data & Knowledge Architect to design and build a scalable knowledge foundation for next-generation K-12 assessment and learning systems. You will work at the intersection of content development, learning science, AI, and psychometrics to ground AI agents and workflows in a structured knowledge layer.

The role emphasizes designing extensible ontologies, data contracts, and APIs, with strong autonomy to prototype, test, and evolve the architecture.

Qualifications

Advanced degree in CS/AI or related field (or equivalent experience).
Hands-on experience designing and building knowledge graphs in production or real-world settings.
Experience with knowledge modeling and semantic schema design (ontologies, vocabularies, taxonomies).
Strong command of graph and semantic technologies and architectural tradeoffs.
Strong data architecture skills including provenance, lineage, and data modeling.
Proficiency in Python or similar languages for building APIs and systems.

Responsibilities

Design and build the knowledge graph and underlying knowledge model for assessment and learning domains.
Represent standards, competencies, skills, claims, evidence, task characteristics, misconceptions, content, and their relationships as computational structures.
Design for extensibility across content areas, programs, assessment contexts, and future AI capabilities.
Establish approaches for stable identity, versioning, provenance, lineage, and lifecycle status of knowledge objects.
Clearly separate authoritative knowledge, expert judgments, derived information, and machine-generated hypotheses.
Evaluate and select graph and semantic technologies based on extensibility, interoperability, reasoning needs, maintainability.
Build the pipelines, mappings, and synchronization processes that connect item banks, standards, metadata, psychometric data, and other sources.
Develop the schemas, APIs, data contracts, and services through which applications access the knowledge architecture.
Partner with software engineers to integrate the knowledge layer into our assessment authoring system.
Design retrieval and context architectures that let AI use structured knowledge effectively (graph, semantic, vector, hybrid).
Create task-specific knowledge projections so each capability receives what it needs.
Maintain traceability between AI outputs and the knowledge used to produce them.
Work with Applied AI Systems Engineers to ensure AI capabilities operate over the shared knowledge layer.
Exercise autonomy in shaping architecture where no blueprint exists.
Prototype quickly, test empirically, and change course when warranted.
Evaluate graph-based, knowledge-grounded, and neuro-symbolic approaches and adopt those with value.
Make architectural decisions and tradeoffs explicit.
Design knowledge systems that are auditable and transparent.
Support AI that strengthens human expertise with expert review points.
Partner with AI engineers, psychometricians, and content experts to test if knowledge designs improve accuracy and reliability.
Establish quality, coverage, and consistency measures for knowledge assets.
Uphold responsible data practices (privacy, security, access control).
Partner with Technology teams and software engineers to integrate the assessment system.
Co-design architecture with AI Science colleagues and Applied AI Systems Engineers.
Collaborate with psychometricians and learning scientists to faithfully represent measurement concepts.
Work with content teams to capture domain knowledge and constraints.
Communicate complex architectural concepts clearly to technical and non-technical audiences.