Director / Principal Product Manager – AGI Knowledge Platform

About SAIGroup

SAIGroup is a private investment firm that has committed $1 billion to incubate and scale revolutionary AI-powered enterprise software application companies. Our portfolio, a testament to our success, comprises rapidly growing AI companies that collectively cater to over 2,000+ major global customers, approaching $800 million in annual revenue, and employing a global workforce of over 4,000 individuals.

SAIGroup invests in new ventures based on breakthrough AI-based products that have the potential to disrupt existing enterprise software markets. SAIGroup’s latest investment, JazzX AI, is a pioneering technology company on a mission to shape the future of work through an AGI platform purpose-built for the enterprise. JazzX AI is not just building another AI tool—it’s reimagining business processes from the ground up, enabling seamless collaboration between humans and intelligent systems. The result is a dramatic leap in productivity, efficiency, and decision velocity, empowering enterprises to become pacesetters who lead their industries and set new benchmarks for innovation and excellence.

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