Role Overview

This role leads the productization of the Knowledge Platform, powering contextual intelligence across the JazzX AGI ecosystem. The knowledge platform provides end‑to‑end capabilities for building and managing dynamic ontologies, knowledge graphs, federated search, and RAG systems connecting enterprise data with advanced reasoning. The product leader defines the strategy, roadmap, and feature execution for how information is ingested, structured, retrieved, and governed at scale. This work ensures AGI systems operate with accurate, grounded, and explainable knowledge. The role partners closely with engineering, design, research, forward‑deployment teams, and enterprise domain experts throughout discovery, definition, validation, and delivery.

Core Responsibilities

Product Strategy

• Build and maintain the roadmap for the full knowledge lifecycle, including ingestion, cleaning, semantic modeling, retrieval, and governance.
• Develop the strategy for dynamic ontologies, evolving schemas, and scalable knowledge graph structures supporting AGI‑level capabilities.
• Guide development of federated search, hybrid retrieval (vector, graph, and keyword), and RAG capabilities tightly integrated with AGI needs.

Platform Productization & Execution

• Translate AGI‑aligned knowledge capabilities into clear product features, workflows, and enterprise‑ready tools.
• Define expectations for ontology builders, graph editors, schema management tools, and knowledge modeling workflows.
• Specify product requirements for indexing, retrieval, permissions, and hybrid search behaviors while partnering with engineering for implementation.
• Ensure the platform meets high standards for reliability, scalability, explainability, and enterprise‑grade security.

Knowledge Platform Integration with AGI Capabilities

• Align platform features with AGI memory, reasoning, grounding, and agent‑driven update flows.
• Define safe and controlled methods for AGI systems to read, assess, and update ontology and graph structures.
• Embed provenance, semantic safety, and alignment safeguards into all knowledge operations.

Customer & Market Validation

• Work with enterprise domain specialists to understand domain structures, workflows, and knowledge governance requirements.
• Deliver builder experiences that enable customer teams and forward‑deployment engineers to model, manage, and extend enterprise knowledge.
• Identify patterns in retrieval quality gaps, knowledge fragmentation, and RAG grounding issues across industries.

Metrics & Impact

• Measure core performance indicators across ingestion quality, ontology accuracy, graph consistency, search precision/recall, RAG groundedness, and update latency.
• Evaluate Knowledge Platform health through drift detection, retrieval reliability analysis, schema evolution safety checks, and end‑to‑end grounding evaluations.
• Track platform adoption, builder workflow effectiveness, and integration quality to drive continuous roadmap decisions.

Qualifications

Experience

• 8+ years in technical product management; 4+ years working with knowledge systems, search, structured data platforms, or ML systems.
• Experience with semantic systems, RAG architectures, information retrieval, knowledge graphs, or large‑scale data infrastructure.
• Background in enterprise AI deployment and distributed systems.

Technical Depth

• Strong understanding of ontology modeling, graph databases, vector search, hybrid retrieval, embeddings, and schema evolution.
• Knowledge of symbolic reasoning, constraints, entity resolution, knowledge fusion, and AGI‑driven inference.
• Familiarity with distributed ingestion/indexing pipelines, multi‑tenant security models, and retrieval optimization techniques.

Leadership

• Ability to lead multi‑year product roadmaps and collaborate effectively with engineering, research, and executive teams.
• Proven skill in converting complex AGI requirements into clear, actionable product plans.
• Ability to mentor and guide junior PMs and cross‑functional partners.

Education

• Bachelor’s degree in Computer Science, Engineering, or a related technical field required. Advanced degrees (MBA, MS, or PhD) are preferred but not required.