THE ROLE

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.

WHAT YOU'LL DO

• 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 issue resolution.
• Partner with Product and customer-facing teams to translate enterprise needs into practical AI capabilities for regulated and data-intensive industries, including financial services, healthcare, insurance, retail, government, and technology.
• Build strong operating rhythms across the US and Prague teams, including planning, technical reviews, architecture forums, talent reviews, incident learning, and roadmap communication.
• Recruit and retain exceptional talent in a competitive market, with an emphasis on developing a balanced team across graph engineering, NLP/ML, data science, quality engineering, and applied research.
• Represent the team with executive stakeholders and communicate complex technical concepts clearly to technical and non-technical audiences.
• Promote a culture of intellectual rigor, customer focus, inclusion, accountability, and continuous learning.
• We are primarily an in-office environment and therefore, you will be expected to work from the Santa Clara office in compliance with Everpure's policies, unless you are on PTO, or work travel, or other approved leave.

WHAT YOU BRING

• 10+ years of experience in artificial intelligence, machine learning, natural language processing, data intelligence, knowledge graphs, or a related technical discipline.
• 5+ years of experience leading and developing engineering, applied science, or data science teams, including experience managing senior technical contributors and/or managers.
• Demonstrated success delivering AI or data intelligence products from research or prototype through production and ongoing operation.
• Strong technical understanding of several of the following areas: knowledge graphs, ontology design, entity resolution, semantic search, GraphRAG, NLP, document intelligence, information retrieval, embeddings, LLMs, model evaluation, or MLOps.
• Practical experience with technologies such as Python, Neo4j or comparable graph databases, vector databases, graph query languages, data annotation platforms, model evaluation frameworks, and cloud-based AI platforms.
• Experience building or operating systems that transform large volumes of structured and unstructured data into governed, searchable, explainable, and actionable information.
• Strong understanding of data and model quality, including evaluation design, precision/recall trade-offs, regression testing, monitoring, drift detection, and production troubleshooting.
• Experience creating scalable processes for training data management, annotation, dataset versioning, model lifecycle management, and quality assurance.
• Experience working with compliance, privacy, security, or governance requirements in regulated or enterprise environments.
• Ability to hire, develop, and retain high-performing teams across geographies and time zones.
• Excellent written and verbal communication skills, with the ability to influence across organizational boundaries and explain technical trade-offs to executive audiences.
• A strategic, inclusive, and pragmatic leadership style, with the judgment to balance near-term delivery with long-term platform and team investments.

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