Senior AI & Automation Specialist
Responsibilities
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
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
Required Qualifications
- 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)
