Everpure
AI Data Intelligence & Knowledge Graph Engineer
About this role
Lead a geographically distributed team of AI/ML and knowledge graph engineers to build enterprise data intelligence platforms that transform unstructured data into AI-ready information. This hands-on leadership role combines technical credibility in NLP, knowledge graphs, and document intelligence with responsibility for team development, strategy, and cross-functional partnerships.
What you'll do
- Build and develop distributed teams across US and Prague locations, managing hiring, performance, and succession planning
- Define technical strategy and roadmap for knowledge graph, document intelligence, and AI-ready data capabilities
- Guide design and implementation of production systems for entity extraction, data classification, semantic enrichment, and GraphRAG patterns
- Establish quality standards and lifecycle discipline covering model training, evaluation, deployment, monitoring, and governance
- Partner with Engineering, Product, Architecture, and Security teams to align priorities and deliver measurable outcomes
- Drive automation and observability across data preparation, training pipelines, and production systems
What they're looking for
- AI/ML and natural language processing leadership
- Knowledge graph and semantic technology architecture
- Team leadership and people development
- Entity extraction and information retrieval
- Model evaluation and quality metrics (precision, recall, F1)
- LLM-assisted workflows and embeddings
- Cross-functional stakeholder management
- Production systems and MLOps
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Everpure
Everpure builds cloud-native storage and data infrastructure solutions, including Kubernetes platforms and enterprise SSD products for mission-critical environments. The company is hiring technical support engineers, validation engineers, and solutions specialists to diagnose complex infrastructure issues, qualify storage hardware, and deliver customer support across cloud and storage technologies.
- Website
- everpure.com
Likely interview questions
- Describe your experience building and scaling distributed teams across different geographies—what practices helped maintain alignment and culture?
- Walk us through a knowledge graph or semantic modeling project you led: how did you handle ontology design and entity resolution challenges?