External Title Engineer -Generative AI Platform and Cortex

Responsibilities

Peraton Labs is seeking a Senior Knowledge Engineer to serve as the program-embedded owner of the knowledge layer that powers a customer-deployed Generative AI Platform. This role sits at the intersection of knowledge engineering, data governance, and customer-facing enablement – keeping the program’s Cortex (knowledge graph, ontology, and curated content) clean, coherent, and connected, and acting as the trusted technical partner to the sector personnel who manage data on the ground.

The position is broad-based and applicable across mission and non-mission domains alike (operations, program management, customer experience, supply chain, finance, compliance, engineering performance, and beyond).

This individual is the program’s knowledge manager, librarian, and connection-maker. They govern what enters the data lake, define how content is described and linked, curate the Cortex and its ontology, and ensure that the relationships between entities, sources, and concepts reflect the way the program actually operates. They translate fluent domain understanding into a living, queryable knowledge structure that analysts, developers, and customer stakeholders can rely on.

As a senior individual contributor, this role sets standards, drives consensus, and mentors others. The Senior Knowledge Engineer works alongside the Data Architect and platform engineering team to ensure the knowledge layer evolves coherently with the underlying data architecture, and provides direct, mission-grounded feedback on platform capabilities and gaps. The ideal candidate brings deep experience in ontology and taxonomy design, knowledge graphs, content curation, and data stewardship – combined with the customer-facing presence to coach sector data managers and represent the program with credibility.

• Own the health and integrity of the program’s Cortex – governing the knowledge graph, ontology, taxonomies, controlled vocabularies, and curated content that the Generative AI Platform draws on.
• Design, evolve, and maintain the ontology and taxonomy: define entities, relationships, properties, and controlled vocabularies that reflect how the program and its customer actually operate.
• Govern data-lake intake – establish and enforce standards for source onboarding, metadata, classification, tagging, quality gates, and retention; decide what enters the lake and Cortex, and on what terms.
• Identify and maintain the connections that make the knowledge layer valuable – cross-source linkages, master/reference data alignment, entity resolution, and relationship enrichment across structured and unstructured content.
• Serve as the program’s knowledge manager and librarian – own the business glossary, content findability, citation discipline, and the lifecycle of knowledge assets from acquisition through retirement.
• Curate Cortex content: deduplicate, retire stale material, manage manifest accuracy, control ontology drift, and ensure provenance and lineage are captured and traceable.
• Provide technical support and coaching to sector personnel who manage data on the ground – helping them publish to standards, troubleshoot data issues, and adopt the metadata and tagging practices that keep the knowledge layer trustworthy.
• Act as the trusted advisor on knowledge architecture decisions – assess current state, identify future state, conduct gap analysis, and recommend prioritization that aligns the knowledge layer to program objectives.
• Collaborate with the Data Architect and platform engineering team to ensure the ontology, knowledge graph, and curation practices integrate cleanly with the underlying data architecture, pipelines, and retrieval systems.
• Partner with analysts (all-source, data, and research) to understand how knowledge is consumed, surface gaps in coverage or connections, and continuously improve retrieval relevance and analytical productivity.
• Define and enforce knowledge-engineering standards, style guides, and SOPs – including ontology change management, naming conventions, source descriptions, and curation workflows.
• Drive consensus across business and technical stakeholders on the knowledge…