Principal Engineer, Knowledge Graph at University of Maryland Global Campus

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Full Time

Start Date

Immediate

Expiry Date

08 Mar, 26

Salary

173000.0

Posted On

08 Dec, 25

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Graph Modeling, Ontology Development, Graph Machine Learning, Semantic Technologies, Neo4j, Data Integration, Python, ML Libraries, Graph Libraries, Databricks, Stakeholder Communication, Team Leadership, Graph Data Cataloging, Entity Resolution, Knowledge Extraction, Graph Query Optimization

Industry

Description

Principal Engineer, Knowledge Graph Data Strategy US Exempt Regular Full time Stateside Exempt 4.4 Location: Stateside Remote We are seeking a strategic and technically proficient Principal Engineer, Knowledge Graph to lead the design, development, and implementation of enterprise-grade knowledge graph solutions. This leader will be responsible for driving semantic data modeling, graph-based AI/ML solutions, and graph database architecture (with an emphasis on Neo4j) in support of our data and AI strategy execution. The ideal candidate brings deep expertise in graph modeling and implementation, ontology development, graph machine learning, and semantic technologies, and is skilled in understanding business goals and requirements and reflecting them in systems implementations that consider current and emerging needs. You will collaborate across data science, engineering, and product teams to embed graph intelligence into enterprise systems using platforms like Neo4j and Databricks. Duties and Responsibilities: Lead the enterprise knowledge graph development and operationalization, including data modeling, ontology and taxonomy development, semantic modeling, and integration with machine learning and business platforms. Design and manage complex graph data models and ontologies using W3C standards (RDF, OWL, SKOS, SHACL) to support data standardization, discovery, and analytics. Develop and implement graph-based machine learning models (e.g., GNNs, link prediction, GraphRAG) to support key business capabilities such as personalized learning, intelligent search, and recommendation systems. Drive the use of NEO4J for graph data storage, querying (Cypher), and integration into enterprise pipelines. Collaborate with AI/ML engineers to fine-tune LLMs and integrate graph data into intelligent agents and semantic search systems. Provide architectural leadership for semantic pipelines and Databricks integration, supporting both batch and real-time graph analytics. Champion the adoption of graph-based AI/ML, especially in online higher education use cases. Translate evolving business needs into a prioritized roadmap for knowledge graph implementation and continuous improvement. Stay abreast of developments in graph database, graph AI/ML, semantic web, agent-based systems, and enterprise AI platforms. Education & Experience Requirements: Education: Master's Degree in Information science, Library and Information Science, Data Science, Ontology, Computer Science, Engineering or a related field. Experience: 6+ years of experience in ontology development, semantics, or graph modeling, including W3C standards (RDF, OWL, SKOS, SHACL). 5+ years of hands-on experience designing and implementing knowledge graphs using NEO4J, including Cypher queries and graph modeling, and graph database design and operations best practices. Proven experience implementing graph-based machine learning models (e.g., GNNs, GraphRAG). Experience working with Databricks and Spark-based data workflows. Demonstrated ability to lead enterprise-level data integration initiatives and semantic modeling projects. Strong programming skills in Python, including ML libraries (e.g., PyTorch, TensorFlow) and graph libraries (e.g., DGL, NetworkX, NEO4J Python driver). Excellent stakeholder communication and team leadership skills. Experience supporting online platforms, ideally in online education or digital business ecosystems. Preferred Requirements: Education: Ph.D. Degree in Information science, Library and Information Science, Data Science, Ontology, Computer Science, Engineering or a related field. Experience: Experience integrating knowledge graphs with LLMs, prompt engineering, and AI agents (e.g., LangGraph, CrewAI). Familiarity with graph query optimization, entity resolution, and knowledge extraction from structured and unstructured data. Experience with graph data cataloging tools (e.g., Stardog, PoolParty, TopQuadrant). All submissions should include a cover letter and resume. The University of Maryland Global Campus (UMGC) is an equal opportunity employer and complies with all applicable federal and state laws regarding nondiscrimination. UMGC is committed to a policy of equal opportunity for all persons and does not discriminate on the basis of race, color, national origin, age, marital status, sex, sexual orientation, gender identity, gender expression, disability, religion, ancestry, political affiliation or veteran status in employment, educational programs and activities, and admissions. Workplace Accommodations: The University of Maryland Global Campus Global Campus (UMGC) is committed to creating and maintaining a welcoming and inclusive working environment for people of all abilities. UMGC is dedicated to the principle that no qualified individual with a disability shall, based on disability, be excluded from participation in or be denied the benefits of the services, programs, or activities of the University, or be subjected to discrimination. For information about UMGC’s Reasonable Workplace Accommodation Policy or to request an accommodation, applicants/candidates can contact Employee Accommodations via email at employee-accommodations@umgc.edu. Benefits Package Highlights: Generous Time Off: Enjoy 22 days of paid vacation, 15 days of sick leave, 3 personal days, and 15 paid holidays (16 during general election years). For part-time employees, time off rates will be prorated based on the number of hours worked. Comprehensive Health Coverage: Access to health care, medical with vision, dental, and prescription plans for both individuals and families, effective from the 1st of the month following your hire date. Insurance Options: Term Life Insurance, Accidental Death and Dismemberment Insurance, and Long-Term Disability (LTD) Insurance. Part-time employees working less than 0.5 FTE are not eligible for LTD. Flexible Spending Accounts: Available for medical and dependent care expenses. Retirement Plans: Choose between the Optional Retirement Program (ORP) or the Maryland State Retirement and Pension System (MSRPS). Supplemental Retirement Plans: include 401(k), 403(b), 457(b), and various Roth options. The university does not provide matching funds. Tuition Remission: Immediate availability for Regular Exempt Staff. Spouses and dependent children are eligible for undergraduate tuition remission after two years of service. NOTE: For part-time employees (at least 50 percent of the time), tuition remission benefits are prorated. Hiring Range: $158,000.00 - $173,000.00 UMGC is not just a great place to learn. It's also a great place to work. The university offers an array of career options, along with ample opportunities for growth and advancement. Explore the broad range of faculty and staff positions and become a member of the UMGC community.

Responsibilities

Lead the design, development, and implementation of enterprise-grade knowledge graph solutions, focusing on semantic data modeling and graph-based AI/ML solutions. Collaborate across teams to integrate graph intelligence into enterprise systems.