Senior AI & Automation Specialist

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

Sample Success Metrics

• 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

This Role is NOT

• 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.

What a Great Week Looks Like

• 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

Qualifications & Skills

• 3+ years of experience in technical operations, business intelligence, automation engineering, or a related field

• Pragmatic AI/automation mindset — you focus on measurable leverage, not hype

• Strong hands-on experience building automation workflows (n8n, Zapier, Make, or custom-built pipelines) with a track record of eliminating manual work at scale

• Proficiency in at least one programming language (Python, Node.js/JavaScript, or TypeScript) with ability to write production-quality scripts and integrations

• Systems integration experience — connecting multiple enterprise platforms (CRMs, project management, content systems) into unified data flows

• Experience designing and building executive dashboards that communicate complex data clearly to leadership audiences

• Working knowledge of the Atlassian suite (Jira, Confluence, Atlas) and CRM systems (Salesforce preferred)

• Excellent documentation and communication skills

• Self-directed and proactive — you identify gaps, propose solutions, and execute without waiting to be told

• Understanding of AI limitations — you know when automation is the wrong answer and when human judgment must remain in the loop

• Experience in the gaming industry or with game publishers/studios

• Familiarity with graph databases (Neo4j) and knowledge graph concepts

• Experience with AI/ML tools and platforms in an applied business context (e.g., Claude, GPT, LLM-based automation)

• Background in NPI (New Product Introduction) frameworks or stage-gate processes

• Experience with data visualization tools (Looker, Grafana, Metabase, or custom React dashboards)

• Experience deploying applications to cloud platforms (Netlify, Railway, Render, Fly.io, or similar)

• Bachelor's degree in Computer Science, Information Systems, Business Analytics, or a related field (or equivalent practical experience)