Senior Product Manager

This role owns the full product lifecycle for QAD's Copilot, Search, and Conversational Analytics capabilities — from architecture through to market launch. You will define what gets built, why it matters to manufacturing users, and how it reaches them: shaping the product vision, driving engineering delivery, and partnering with GTM to ensure adoption.

Responsibilities

Own the full product lifecycle for QAD's Copilot, Search, and Conversational Analytics capabilities — from architecture through to market launch; Define the semantic search and conversational analytics product that allows manufacturing users to query operational data — orders, inventory, suppliers, quality records — in natural language; Shape the product vision, drive engineering delivery, and partner with GTM to ensure adoption; Own the intelligence contract: how queries are understood, manufacturing context assembled, retrieval orchestrated, and responses grounded in governed data; Drive the Copilot from concept to customer; Build feedback loops that make the product smarter over time; Establish QAD's conversational analytics presence in market; Define the grounding contract ensuring every Copilot response cites governed manufacturing data; Drive conversational analytics strategy across manufacturing data domains; Specify the disambiguation model; Own product definition for QAD's semantic search layer; Define search intent taxonomy and result formats by intent type; Drive federated search architecture; Maintain a structured customer discovery programme; Own the product backlog, writing stories to Staff Engineer level of specificity; Partner with GTM to define launch and adoption strategy for Copilot and Search; Define quality framework for retrieval relevance, grounding rate, latency distribution; Proactively surface dependencies, risks, and scope changes with resolution proposals; Communicate product and architecture decisions clearly to non-technical stakeholders; Build credibility with Engineering, Architecture, and GTM through quality and precision of written work

Requirements

7–12 years in product management, with significant time owning AI-powered, search, or analytics products in enterprise B2B environments; Proven track record owning the full product lifecycle: customer discovery, specification, engineering delivery, and GTM; Experience working in matrixed organisations; Evidence of structured discovery practice applied to a technical product surface; Semantic search architecture: indexing strategy, query understanding, entity recognition, ranking models, and relevance evaluation at enterprise scale; Conversational AI product design: translating natural language queries into structured analytical results across complex, domain-specific data; LLM-integrated product specification: grounding strategies, hallucination mitigation, response quality gates, and context window management; Data platform fluency: OLAP vs operational DB reads, federated search across heterogeneous stores, and data freshness constraints; SQL and data modelling to validate query plans, understand schema implications, and reason through retrieval architecture trade-offs; Working familiarity with intent detection and query understanding pipelines; Working familiarity with context assembly and packaging; Working familiarity with session and state management; Working familiarity with human-in-the-loop design; Writes first, asks second; Distinguishes clearly between what is known, assumed, and needs validation; Comfortable operating without a playbook; Gravitates toward precision in language and specification

Nice to have

Familiarity with manufacturing ERP data models (inventory, orders, procurement, quality); Experience with operational analytics or supply chain intelligence products

Benefits

Opportunities for growth; QAD DEI program; Equal Opportunity Employer; Clear growth trajectory from Staff PM to broader intelligence surface ownership