AI Go-to-Market Engineer

Flosum is a Salesforce-native DevOps and data protection platform serving enterprise Salesforce teams across the Fortune 5000. We are in wartime. Pipeline generation, retention, and product velocity are our three highest-priority problems, and AI is the lever that accelerates all three. We are hiring our first AI Go-to-Market Engineer to architect, deploy, train, and continuously optimize AI agents and automation across the entire revenue engine — from first touch to closed-won to expansion.

This is not a "prompt engineering" role. This is a builder role. You will sit at the intersection of revenue operations, sales, marketing, and customer success, and your job is to make every human on the GTM team dramatically more productive by deploying AI systems that generate pipeline, accelerate deals, and reduce churn. You report directly to the CEO.

WHAT THIS ROLE MUST DO TO CHANGE FLOSUM'S TRAJECTORY

1. Deploy and personally train at least one AI SDR agent within the first 30 days — focused on either inbound qualification from Qualified/Marketo or outbound prospecting via Outreach — and run weekly refinement cycles until it outperforms the bottom half of the human BDR bench.
2. Build an AI-powered inbound qualification system that captures, scores, and routes every website visitor and form fill in real time so no lead waits, no lead gets lost, and AEs only touch prospects that are ready to buy.
3. Increase revenue-generating time per AE from the current ~25% to 50%+ by automating CRM hygiene, call summarization (via Gong), follow-up sequencing, proposal drafting, and pipeline updates — eliminating every minute of admin work that doesn't directly produce revenue.
4. Architect the AI layer across Flosum's GTM stack (Salesforce, Marketo, Outreach, Qualified, Gong) so that data flows between systems without manual intervention, every customer touchpoint is captured, and AI agents share learnings in a virtuous loop with Salesforce as the hub.
5. Build and maintain an AI-driven competitive intelligence engine that monitors our competition, pricing changes, and product launches — and feeds structured insights to sales and product weekly.
6. Create hyper-personalized outbound campaigns at 10x current volume without proportional headcount increase — using AI to research accounts, generate personalized messaging based on ICP signals (Salesforce team size, current DevOps tooling, compliance requirements like DORA), and sequence multi-channel touches across email, LinkedIn, and phone.
7. Instrument a closed-loop data feedback system between BDR activity, pipeline outcomes, and AI agent performance so that ICP targeting, messaging, and channel mix improve every week based on actual conversion data — not guesswork.
8. Deploy AI-assisted customer health scoring and churn prediction models that flag at-risk accounts before renewal, feeding Terry's CS team with actionable intervention triggers tied to usage data, support ticket patterns, and engagement signals.
9. Prototype AI-powered product-led growth touchpoints — including conversational qualification on the website, self-serve onboarding assistance, and automated nurture for trial users — supporting the PLG motion as it matures.
10. Evaluate, select, and deploy no more than two AI vendors per use case using a structured bakeoff methodology: reference checks via email, FDE conversations before contract, and 30-day dedicated training commitments — no lazy agency deployments.