Senior Product Manager, AI Knowledge Graph Platform

Cisco; Austin (TX); On-site; USD 179,000 - 254,000; Full time

Benefits offered by this job

Medical insurance

401(k) with company match

Parental leave

RSU grants

Job summary

Cisco Data Fabric is shaping the data foundation for enterprise telemetry and AI. You will drive a significant product area, translating customer needs into a clear roadmap, requirements, and measurable outcomes across semantic search, retrieval-augmented generation, and agentic AI workflows.

Collaborate with engineering to deliver graph schemas, metadata integrations, and scalable data-processing pipelines while guiding adoption and ongoing improvements.

Qualifications

• 11+ years of experience in product management or equivalent technical product role.
• 3+ years working on enterprise data, AI/ML, search, metadata, or platform products.
• Experience delivering enterprise software or data-platform capabilities from discovery to launch.

Responsibilities

• Own a significant product area within the AI Knowledge Graph with a clear roadmap.
• Define capabilities that connect telemetry, assets, topology, and metadata for semantic search and AI workflows.
• Partner with engineering to deliver graph schemas, metadata integrations, and scalable pipelines.
• Drive lifecycle from discovery through launch, adoption, and continuous improvement.

Skills

Product management

Enterprise data

AI/ML collaboration

Knowledge graphs

Graph databases

Metadata systems

Vector search

Retrieval-augmented generation

Spark/Distributed data

Education

Bachelor's degree

Tools

Neo4j

Amazon Neptune

RDF/OWL

GraphRAG

LangChain

Vector databases

Job description

Cisco Data Fabric is shaping the data foundation for enterprise telemetry and AI. You will drive a significant product area, translating customer needs into a clear roadmap, requirements, and measurable outcomes across semantic search, retrieval-augmented generation, and agentic AI workflows.

Collaborate with engineering to deliver graph schemas, metadata integrations, and scalable data-processing pipelines while guiding adoption and ongoing improvements.