Senior AI Ecosystem Intelligence & Automation Architect

Job Description: The Senior AI Ecosystem Intelligence & Automation Architect will design and operationalize AI-enabled capabilities that improve the scalability, accuracy, discoverability, reporting, and operational efficiency of Intel Edge AI and Robotics ecosystem programs. The role uses AI, data architecture, metadata, data extraction, analytics, and automation to turn ecosystem program activity into trusted operational intelligence and repeatable workflows.   The successful candidate will help build the intelligence and automation layer supporting ecosystem programs, customer discovery, onboarding, qualification, catalog operations, partner engagement, and internal reporting. This includes automated intake and validation workflows, ecosystem data extraction and normalization, AI-powered search and recommendations, customer satisfaction insights, operational dashboards, and AI-assisted reporting that supports both internal stakeholders and external customer experiences.   This role is expected to work as part of a central design and operations team where UX, program operations, ecosystem strategy, AI enablement, and development execution must connect. The role is not limited to AI feature design. Its primary purpose is to make ecosystem operations more scalable, measurable, automated, and customer-responsive through applied AI and trusted data.

Focus on information architecture, metadata, taxonomy, search optimization, knowledge management, and AI/RAG readiness to support the organization’s AI-driven discovery and ecosystem platform strategy. Design AI-enabled operational capabilities that automate manual ecosystem program workflows, including intake, onboarding, validation, status collection, content classification, and reporting preparation.   Define ecosystem data and metadata requirements that support   accurate   search, filtering, recommendations, customer-facing discovery, internal reporting, and AI-assisted workflows.   Architect data extraction and normalization approaches that convert partner, program, catalog, qualification, and engagement data into usable ecosystem intelligence.   Develop requirements for AI-powered search, recommendations, chatbot   assistance , RAG-ready content structures, guided workflows, and agentic process automation.   Create operational reporting requirements and dashboards for ecosystem engagement, customer satisfaction, onboarding health, qualification status, content quality, and program execution visibility.   Identify   opportunities to use AI to reduce manual effort,   eliminate   repeat work, improve data quality, accelerate customer response, and scale ecosystem program operations.   Partner with UX, program operations, ecosystem strategy, and development teams to ensure AI-enabled operational capabilities are grounded in real user journeys and program execution needs.   Work with development teams to translate AI, automation, data, and reporting requirements into buildable specifications, acceptance criteria, and quality expectations.   Establish data quality checks, automation rules, and operational feedback loops that keep ecosystem information correct, current, searchable, and   decision-ready .   Analyze customer engagement, search behavior, satisfaction signals, and operational process data to recommend improvements to ecosystem experiences and workflows.   AI-Centered Operational Deliverables  AI-assisted ecosystem search and recommendation requirements that improve internal and external discovery.   Automated data extraction and normalization requirements for catalog, qualification, partner, and ecosystem program inputs.   Operational automation requirements for intake validation, onboarding readiness, lifecycle tracking, and program status collection.   Customer satisfaction and engagement intelligence requirements that   identify   friction, gaps, and improvement opportunities.   Ecosystem reporting