AI Engineer – Marketing & GTM Systems

Outbound engine: lead sourcing → enrichment → scoring → personalized sequencing, wired into our CRM. Agentic where possible, human-in-the-loop where it matters.

Inbound & lifecycle flows: lead routing, qualification, nurture triggers, activation and re-engagement automations.

Content pipeline: AI-assisted production for outbound copy, landing pages, social, and campaign assets — with QA gates so nothing off-brand ships.

GTM data layer: dashboards and reporting that show pipeline, conversion, and campaign performance without anyone assembling them by hand.

Temporary fixes: when a GTM workflow breaks or a tool gap appears, you build the patch fast rather than waiting on a vendor.

Day 7: first shippable artifact live — a working flow, not a plan.

Day 30: 2–3 GTM automations in production; outbound engine in alpha.

Day 60: measurable scope absorption — which manual GTM workflows are you removing per week?

Day 90: trial decision, based on shipped output and hours-saved/pipeline-moved numbers.

Ship fast with LLM APIs (Claude, GPT) — prompt engineering, agent frameworks, RAG where useful

Strong Python and/or TypeScript; comfortable with scraping, enrichment, and API integrations

Hands-on with the modern GTM stack: CRM (HubSpot/Salesforce-type), enrichment (Clay/Apollo-type), orchestration (n8n/Make/Zapier), email infra

Have built and can demo autonomous or semi-autonomous workflows that ran in production — links or Looms beat resumes

Self-directed. You find the manual workflow, propose the fix, and ship it. Raising a problem without a proposed fix is not how we work.