Principal Solutions Architect – Expert Services

🕒 vor 1 Monat

🇺🇸 Vereinigte Staaten – Remote

💵 $150.000 - $290.000 / Jahr

⏰ Vollzeit

🔴 Experte

💻 Lösungsingenieur

👻 Geisterscore 0%

Was es bedeutet: wie wahrscheinlich diese Stelle offen bleibt, ohne dass aktiv eingestellt wird.So liest du es: 60 % oder höher heißt, wir halten das für einen Geisterjob. Niedrigere Werte sind weniger bedenklich.Wie wir werten: Dauer offen, Neuveröffentlichungen, offene Stellen vs Teamgröße, wie schnell Stellen geschlossen werden, und ob ein Gehalt angegeben ist.Hinweis: eine Schätzung aus öffentlichen Daten, kein Urteil über das Unternehmen. Echte Stellen können hoch punkten, nutze es also als Hinweis, nicht als Fakt.

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Mendix

WebsiteLinkedInAlle Stellen

1001 - 5000 Mitarbeiter

Gegründet 2005

💼 Beratung

🏥 Gesundheitswesen

🏭 Fertigung

💰 €25.000.000 Series B im 2014-01

Consulting • Healthcare • Manufacturing

Mendix ist eine führende Low-Code-Entwicklungsplattform für Unternehmensanwendungen und wurde im Magic Quadrant 2024 von Gartner anerkannt. Sie ermöglicht es Unternehmen, die Anwendungsentwicklung und -bereitstellung durch KI-unterstützte Funktionen zu beschleunigen. Mendix bietet eine visuelle Entwicklungsumgebung namens Studio Pro, die komplexe Aufgaben vereinfacht und Routineprozesse automatisiert. Die Plattform unterstützt IT-Governance, Compliance und digitale Transformationsstrategien. Mendix hilft Organisationen, ihre Altinvestitionen zu optimieren und außergewöhnliche Kundenerlebnisse in Branchen wie Finanzdienstleistungen, Fertigung und Einzelhandel zu schaffen. Darüber hinaus bietet die Plattform robuste Integrationsmöglichkeiten und Zusammenarbeit mit strategischen Partnern wie AWS und Red Hat.

Beschreibung

• Lead end-to-end delivery of strategic customer programs from discovery through production deployment and operationalization. • Serve as the primary technical leader across multiple concurrent enterprise engagements. • Drive alignment across customer stakeholders, implementation partners, product teams, and engineering organizations. • Ensure projects are delivered on time, within scope, and aligned with customer business objectives. • Provide executive-level guidance on AI transformation, enterprise intelligence, and operational modernization initiatives. • Collaborate with product, presales, and delivery teams to translate business use cases into knowledge graph-based architectures, reference implementations, and solution blueprints. • Design scalable enterprise architectures leveraging Intelligence Center X, AI Studio, Mendix, Graph, and cloud-native technologies. • Architect enterprise knowledge graph solutions that connect business processes, operational systems, products, data assets, and organizational knowledge. • Design graph-based semantic data models that support enterprise intelligence, AI reasoning, contextual awareness, and relationship analytics. • Apply architectural standards for security, governance, scalability, observability, and operational excellence. • Lead the design and implementation of enterprise knowledge graphs using RapidMiner Graph Studio demonstrating expertise in conceptual modeling, ontology design, graph data management, query and inference techniques, and the engineering practices required to deploy and govern large-scale, production grade knowledge graph systems. • Establish best practices for performance, governance, scalability, and operational management. • Enable customers to transform disconnected enterprise data into connected intelligence assets. • Architect and implement agentic workflows that automate complex business processes and decision-making activities. • Guide customers in identifying and prioritizing high-value AI use cases. • Design enterprise intelligence solutions that combine structured and unstructured data using knowledge graphs and AI services. • Establish governance models for AI agents, knowledge management, data access, and model utilization. • Design graph-powered retrieval and contextual reasoning architectures that improve the effectiveness of enterprise AI agents. • Drive adoption of AI-powered automation while ensuring responsible, secure, and compliant implementation practices.

🎯 Anforderungen

• 12+ years of experience in enterprise software delivery, solution architecture, consulting, or digital transformation leadership. • 5+ years of hands-on experience designing and implementing graph database solutions and enterprise knowledge graphs. • Deep expertise in enterprise architecture, systems integration, cloud platforms, and distributed systems. • Strong experience with graph data modeling, ontology design, semantic architectures, and relationship analytics. • Experience with competitive graph database technologies such as Neo4j, Amazon Neptune, TigerGraph, Stardog, ArangoDB, or similar platforms. • Knowledge of graph query languages, ideally including SPARQL, Cypher, Gremlin, or equivalent technologies. • Experience leading large-scale technology implementations for Fortune 1000 organizations. • Strong understanding of AI, machine learning, generative AI, agentic AI, and enterprise automation platforms. • Experience with context engineering strategy: what information is loaded, how it is chunked, summarized, ordered, and governed across tools, skills, and knowledge sources. • Demonstrated success managing executive stakeholder relationships and complex delivery programs. • Exceptional communication, facilitation, leadership, and consulting skills. • Bachelor's degree in Computer Science, Engineering, Information Systems, or a related field. • Preferred Qualifications • Experience in manufacturing and Product Lifecycle Management (PLM), including product structures, bills of materials (BOMs), engineering change management, and digital thread initiatives. • Experience implementing graph-based digital thread, product intelligence, or manufacturing knowledge graph solutions. • Familiarity with semantic web standards, RDF, OWL, linked data, and knowledge representation frameworks. • Experience with cloud platforms including AWS, Azure, or Google Cloud. • Experience implementing Retrieval-Augmented Generation (RAG), agent frameworks, and enterprise AI architectures. • Knowledge of enterprise data governance, master data management, and metadata management practices. • Advanced degree in Computer Science, Engineering, Data Science, Information Systems, or a related field. • Relevant cloud, architecture, AI, or graph technology certifications.