Senior AI Ecosystem Intelligence & Automation Architect

About This Role

• 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.
• Key Responsibilities 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 framework covering program health, customer usage, qualification status, content quality, and operational performance.
• Data quality and governance rules that ensure AI outputs, search results, dashboards, and customer-facing ecosystem information stay reliable.
• AI-enabled self-service and workflow recommendations that reduce manual support and improve customer completion rates.