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xsolla

Intelligence and Automation Lead

Department
Engineering
Job Type / Location
Los Angeles
Experience Required
8+ years
Posted On

About the Role

This is a hands-on builder role from day one. You will write code, build pipelines, and ship automation every week. This is not a strategy-only position.

Responsibilities

  • Own the Intelligence and Automation function for GSIP and Web3 PS — design, build, and maintain automated workflows (n8n or similar) for meeting notes processing, trip reports, intake routing, and reporting
  • Develop and maintain integrations across Salesforce, Jira, Confluence, Atlas, and Neo4j to create a unified intelligence layer
  • Design and build executive dashboards that surface real-time portfolio health, deal pipelines, partnership progress, and KPIs for leadership across both divisions
  • Build and maintain Confluence-based intelligence pages — partner profiles, initiative trackers, competitive intelligence, and automated content pipelines
  • Support the company's operating framework that separates strategic narrative, operational process, and intelligence/automation — building workflows around stage gates, milestone tracking, approvals, and templates
  • Drive AI adoption across both divisions, identifying opportunities to increase operational efficiency through Claude, Neuronet, and other AI tools
  • Own the Technical Strategy Roadmap for GSIP and Web3 PS, setting the long-term vision for automation and intelligence infrastructure
  • Establish cadences for weekly reporting, monthly optimization reviews, and quarterly ROI reporting
  • Measure and communicate the leverage gained through technology investments
  • Continuously scout emerging AI capabilities, models, and tools on a weekly cadence. Run rapid experiments and present findings to the team
  • Conduct regular demo sessions and hands-on training to ensure every team member across both divisions can effectively leverage AI tools. Lead by showing, not telling
  • Attend key GSIP and Web3 PS meetings and working sessions to deeply understand operational context. Solutions must emerge from firsthand knowledge of how the team works
  • Once automation is validated, hand off to operations leadership for integration into standard operating workflows. You pioneer; they scale
  • Establish and maintain AI governance practices — ensuring AI decisions are traceable, compliant, and reversible
  • Build predictive models for deal outcomes, partnership health, and initiative success. Surface anomalies and patterns before they become problems

View Assessment Process

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