AI Data Intelligence & Knowledge Graph Engineer

Everpure (NYSE: P) has evolved from storage pioneer to data platform, closing fiscal 2026 with $3.7 billion in revenue, its first billion-dollar quarter, and accelerating growth into FY27. Our strategic agenda spans the companies defining the next era of technology - hyperscalers, AI labs, the AI hardware supply chain, data platform providers, and the broader AI ecosystem.

This type of work—work that changes the world—is what the tech industry was founded on. So, if you're ready to seize the endless opportunities and leave your mark, come join us.

We are seeking an AI Data Intelligence & Knowledge Graph Engineer to lead a distributed organization of AI/ML, NLP, knowledge graph, and data intelligence professionals in the United States and/or Prague. This leader will be responsible for building and scaling the team, setting technical direction, and delivering production-grade capabilities that transform unstructured and structured enterprise data into accurate, contextual, governed, and AI-ready information.

The role sits at the intersection of applied AI, machine learning, document intelligence, semantic modeling, and enterprise data management. You will guide teams working on knowledge graph construction, ontology and taxonomy development, entity and relationship extraction, document classification, retrieval, model optimization, and AI-assisted workflows. You will also establish the operating mechanisms, quality bar, and cross-functional partnerships needed to move these capabilities from research and experimentation into reliable enterprise products.

This is a hands-on people leadership role for a technically credible manager who can operate across geographies, connect strategy to execution, and develop strong leaders and senior individual contributors. The successful candidate will be equally comfortable discussing model quality, graph architecture, production reliability, talent strategy, and customer outcomes.

• Lead and develop a geographically distributed team in the United States and Prague, including hiring, onboarding, coaching, performance management, succession planning, and organizational design.

• Establish a clear team strategy and roadmap for AI data intelligence, knowledge graph capabilities, document intelligence, and AI-ready data workflows.

• Partner with senior leaders in Engineering, Product Management, Architecture, Security, Quality, Customer Success, and Go-to-Market to align priorities and deliver measurable outcomes.

• Guide the design and implementation of production systems that extract entities and relationships, classify and enrich data, build semantic context, and make information useful for AI models, agents, and enterprise applications.

• Lead technical direction across knowledge graph and semantic technologies, including ontology and taxonomy design, entity resolution, graph analytics, graph-based retrieval, and GraphRAG patterns.

• Oversee AI/ML approaches for document intelligence, named entity recognition, information retrieval, classification, embeddings, LLM-assisted workflows, and domain-specific extraction.

• Create a disciplined model and data lifecycle covering dataset generation, annotation, training, evaluation, deployment, monitoring, retraining, and governance.

• Establish quality and reliability standards using measures such as precision, recall, F1 score, accuracy, retrieval effectiveness, false-positive and false-negative rates, drift, and customer-reported outcomes.

• Ensure explainability, traceability, documentation, and responsible-use practices are built into AI systems and workflows from design through production.

• Drive automation across data preparation, training pipelines, regression testing, model evaluation, deployment, observability, and production