What you'll own
Qualified lead growth to publitas.com from organic and AI-search channels — this is the metric you're measured on.
SEO performance: technical health, rankings, and content quality on the pages that drive pipeline.
GEO/AEO: getting Publitas cited in AI-generated answers for our category, and measuring citation share over time.
Lead attribution from day one: instrumenting organic and AI-referred traffic through to qualified leads, so we can prove what's working rather than report traffic in isolation.
The automation: designing and building the system that scales monitoring, content, and optimization. We have a hypothesis that much of this can run as an agent-based engine with human-in-the-loop review — you'll validate that, propose the architecture, and build what actually moves the lead number.
How you'll work (AI-first)
AI-native execution: you use LLMs to work faster and to build systems, always behind human review gates — nothing auto-publishes unreviewed.
You document and hand off what you build. We're not buying a black box only one person can run.
Engagement
This starts as a flexible-hours contract with a clear path to full-time. The first phase is a proof window: get attribution instrumented, ship the first automation, and show early movement in AI-search visibility and qualified-lead signals. Deliver against that and we convert the role to full-time. We commit when the results do.
At Publitas, we see AI as a core lever for performance, efficiency, and innovation. We expect all team members to actively use AI in their daily work to improve quality, speed, and output.
Depending on the role, this ranges from effectively using AI tools in day-to-day workflows to designing and scaling AI-driven systems. We are not looking for candidates who are “AI-curious”; we look for people who already use AI to do better work and can demonstrate tangible impact.
