Note: The job is a remote job and is open to candidates in USA. Peraton is a next-generation national security company that drives missions of consequence spanning the globe and extending to the farthest reaches of the galaxy. They are seeking a Senior Knowledge Engineer to manage the knowledge layer for a customer-deployed Generative AI Platform, ensuring data governance and serving as a technical partner for sector personnel.
Responsibilities
- 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 architecture vision, roadmap, and tradeoffs; influence the program and customer to make sound long-term decisions
- Provide continuous, well-articulated feedback to platform engineering and product teams on capability gaps, retrieval quality, ontology tooling, and curation workflows that would unlock additional program value
- Document the knowledge architecture, ontology decisions, intake standards, and curation methodologies so the capability is transferable and not dependent on a single individual
- Mentor junior knowledge engineers, data curators, and data stewards; build the program’s knowledge-engineering bench through coaching, code/model review, and shared best practices
- Support training and onboarding of analysts, engineers, and sector personnel on how to use, contribute to, and trust the Cortex
- Meets directly with program leadership, sector data managers, and customer stakeholders to identify knowledge needs, intake priorities, and curation requirements
- Works within overall program plans and delivery cadences; aligns ontology and curation work to platform release cycles
- Provides feedback to customers and creates structured documentation, including ontology specifications, intake standards, curation playbooks, and status reports
- Advises program and customer leadership on knowledge-architecture configuration and implementation options based on industry best practices
- Leads or supports the customization, implementation, testing, and deployment of ontology updates, taxonomy changes, and Cortex curation workflows
- Acts as a technical mentor for the program team and customer in transferring knowledge-engineering expertise
- Ensures that knowledge-engineering deliverables are complete, traceable, and timely
- Generates timely status reporting on Cortex health, intake throughput, curation backlogs, and knowledge-quality metrics
Skills
- Minimum of a Bachelor's degree in Information Science, Library & Information Science, Computer Science, Data Science, Knowledge Management, Linguistics, Computational Linguistics, or a related field; Master's degree preferred
- 8–12 years of relevant experience in knowledge engineering, ontology/taxonomy development, knowledge graph curation, data stewardship, information architecture, or comparable senior knowledge-management roles
- Demonstrated experience designing and maintaining ontologies, taxonomies, and controlled vocabularies in production environments — not just as one-time deliverables
- Demonstrated experience curating and governing a knowledge graph or comparable structured knowledge asset, including entity resolution, relationship modeling, and ontology change management
- Demonstrated experience governing data intake into a lake, warehouse, or comparable repository — including source onboarding, metadata standards, classification, and quality gates
- Strong grounding in data stewardship and governance practices — business glossaries, lineage, provenance, retention, and access control — with the ability to apply them pragmatically
- Working proficiency in SQL and a scripting language (Python preferred) sufficient to inspect data, profile sources, validate curation outcomes, and automate routine knowledge-engineering tasks
- Familiarity with knowledge representation standards and tooling (e.g., RDF, OWL, SKOS, SHACL, property graphs, Cypher/Gremlin, or comparable) and pragmatic judgment about when to apply them
- Strong critical thinking and problem-solving skills, including the ability to reconcile conflicting source definitions, resolve ambiguity, and impose structure on messy unstructured content without losing fidelity
- Customer-facing presence and judgment — the ability to coach sector data managers, build trust quickly, and represent the program professionally
- Strong written and verbal communication skills, including the ability to brief executive and customer audiences and to author clear specifications, standards, and methodology documents
- Comfort operating in fast-paced, evolving environments where tools, ontologies, and workflows are actively being developed and refined
- Ability to work cross-functionally with architects, developers, and analysts, and to provide clear, prioritized feedback on platform capabilities and knowledge-engineering needs
- US Citizenship with the ability to obtain and maintain required security clearances or suitability determinations as the program requires
- Hands-on experience with AI-enabled platforms, large language models, retrieval-augmented generation (RAG), agentic AI workflows, or AI-assisted curation and enrichment workflows
- Experience curating knowledge for LLM consumption — chunking strategies, embedding hygiene, retrieval evaluation, and grounding/citation discipline
- Experience with graph databases (Neo4j, Kuzu, Amazon Neptune, TigerGraph, or comparable) and graph query languages (Cypher, Gremlin, SPARQL)
- Experience with metadata management, data catalog, or governance platforms (Collibra, Alation, Atlan, DataHub, Apache Atlas, or comparable)
- Familiarity with formal knowledge-management frameworks (DAMA-DMBOK, DCAM, FAIR data principles) and the judgment to apply them pragmatically
- Experience with NLP techniques relevant to knowledge engineering — named entity recognition, relation extraction, coreference resolution, topic modeling — at a working rather than research level
- Background in domains beyond intelligence — such as commercial operations, federal civilian programs, healthcare, financial services, supply chain, customer experience, or engineering program management — where knowledge rigor and customer trust are equally critical
- Experience embedding with a customer or program team for an extended period and being recognized as a trusted advisor rather than an external contributor
- Experience developing ontology style guides, curation SOPs, intake standards, training materials, or knowledge-engineering playbooks
- Experience evaluating or adopting new knowledge-engineering or AI tooling, including participation in pilot programs, technology transitions, or capability assessments
- Mentorship experience — coaching junior knowledge engineers, curators, or stewards and contributing to team growth
- Exposure to Agile delivery, sprint-based curation cadences, and cross-functional team collaboration
Benefits
- Depending on the position, employees may be eligible for overtime, shift differential, and a discretionary bonus in addition to base pay.
Company Overview