onsite
Intelligence & Automation Engineer
Intelligence and Automation Engineer
The Intelligence & Automation Engineer is a hands-on role responsible for designing, building, and maintaining automated workflows and integrations to create a unified intelligence layer. This position involves developing executive dashboards, managing technical strategy, and driving AI adoption across divisions to enhance operational efficiency and build predictive models.
About the role
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