Senior AI & Automation Specialist

Xsolla is a global commerce company with robust tools and services to help developers solve the inherent challenges of the video game industry. From indie to AAA, companies partner with Xsolla to help them fund, distribute, market, and monetize their games. Grounded in the belief in the future of video games, Xsolla is resolute in the mission to bring opportunities together, and continually make new resources available to creators. Headquartered and incorporated in Los Angeles, California, Xsolla operates as the merchant of record and has helped over 1,500+ game developers to reach more players and grow their businesses around the world. With more paths to profits and ways to win, developers have all the things needed to enjoy the game.

For more information, visit xsolla.com.

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.

• 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

• Automation coverage percentage — share of cross-divisional workflows with automation vs. manual execution

• Manual effort reduction — measurable hours saved per week/month through automation

• Cycle time compression — faster turnaround on reporting, meeting notes, intake processing, and partner intelligence

• Leverage ROI — demonstrable return on technology investments relative to time and cost invested

• Dashboard adoption — percentage of leadership actively using intelligence dashboards for decision-making

• AI-assisted quality improvement — reduction in errors, rework, and inconsistencies through automated validation

• A tool collector — adopting every shiny new AI tool without measuring impact

• IT support — this is a strategic builder role, not a help desk

• A disconnected experiment lab — you must be embedded in the team's daily reality

• A process designer — operations leaders own workflow design; you automate within their frameworks

• A pure data science role — you build production systems that deliver daily value, not research models

• Disqualifiers: "AI will solve everything" mentality, tool-first thinking without business context, inability to measure impact quantitatively.

• Monday: Scout 3 new AI capabilities released that week

• Tuesday: Demo a prototype automation to the team

• Wednesday: Ship an integration that eliminates 2 hours of manual work

• Thursday: Present a dashboard insight that changes a leadership decision

• Friday: Hand off a validated automation to operations leadership for scaling