Product Director - AI Knowledge Graph & Intelligent Data Pro

The application window is expected to close on: 04/01/2026 Job posting may be removed earlier if the position is filled or if a sufficient number of applications are received. Product Director - AI Knowledge Graph & Intelligent Data Pro At Splunk, now part of Cisco, we’re redefining how machine data powers the AI-driven enterprise. Cisco Data Fabric connects, contextualizes, and activates data across hybrid environments, fueling agentic AI and trusted, enterprise-grade intelligence. We’re seeking a product Director to lead the roadmap and strategy for machine data Knowledge Graph and Intelligent Data Processing, integrating Spark-powered processing with semantic graph intelligence. This role sits at the heart of Splunk’s Data Fabric and Machine Data Lake initiatives driving how telemetry becomes knowledge and powering AI-native experiences across Security, Observability, and IT domains. About the Role This IC-level Product Director will define and deliver the AI Knowledge Graph platform, the intelligence layer that connects machine data, metadata, and semantics for reasoning agents and contextual analytics. You’ll integrate this with our Spark-based processing layer to enable graph-enriched vector search, RAG pipelines, and agentic AI workflows at scale. This role is focused on AI, graph, and metadata intelligence, and distributed Spark data processing to operationalize the knowledge graph for high-scale AI use cases. Key Responsibilities Define the AI Knowledge Graph product strategy across Splunk’s Data Fabric, contextualizing telemetry, metadata, and semantics into a machine-intelligence foundation. Drive the product strategy and roadmap for graph-enabled data processing, reasoning, and enrichment pipelines integrated with Spark-based workloads. Build semantic linking between telemetry, topology, assets, and business entities, enabling context-aware AI and multi-agent reasoning. Partner with engineering and architecture teams to integrate graph and vector data into AI pipelines, enabling retrieval-augmented generation (RAG), intelligent federation, and AI-native observability. Lead product definition for graph schema, ontology management, and metadata catalog integration, ensuring interoperability with open standards and Splunk-managed repositories. Define data processing and enrichment workflows employing Spark for scalable ingestion, transformation, and embedding creation. Collaborate cross-functional (AI/ML, data platform, federated analytics, and security) to translate CDF strategy into technical product outcomes. Ensure alignment with Cisco’s AI ecosystem, bridging machine data with enterprise context for trusted AI operations. Qualifications 15+ years of product management experience, including 5+ years in AI, knowledge graph, or data platform leadership. Deep understanding of AI/ML systems, graph-based reasoning, LLM integration (RAG, agents), and ontology-driven metadata models. Experience with Spark or distributed data processing frameworks supporting AI pipelines and large-scale graph operations. Strong technical fluency in vector databases, graph stores, knowledge representation, and hybrid semantic search. Preferred Qualifications Proven ability to partner closely with engineering and architecture to translate vision into scalable, production-grade systems. Exceptional communication skills—able to articulate how machine data becomes knowledge, and knowledge becomes AI-driven action. Experience with Lang Chain, Graph RAG, Neo4j, Amazon Neptune, or RDF/OWL frameworks. Familiarity with Cisco AI ecosystem, Splunk MCP, or federated data architectures. A background in observability, security, or enterprise data intelligence is a strong plus. Why Cisco? At Cisco, we’re revolutionizing how data and infrastructure connect and protect organizations in the AI era – and beyond. We’ve been innovating fearlessly for 40 years to create solutions that power how humans and technology work together across the physical and digital ... (truncated, view full listing at source)