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

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.
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.
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.
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.
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.
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.
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.
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.
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.
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.

Requirements

Hands-on experience deploying and training AI SDR/BDR agents in a real B2B sales environment — not evaluating, not advising, actually deploying and iterating weekly for at least 90 days.

Deep working knowledge of the modern B2B GTM stack — specifically Salesforce (as CRM and data hub), Marketo or equivalent MAP, Outreach or Salesloft, Gong or Chorus, and real-time chat/qualification tools like Qualified or Drift.

Ability to write and maintain production integrations — Python, JavaScript, or equivalent — connecting AI agents to CRM, MAP, and sequencing tools via APIs. You don't need to be a software engineer, but you need to ship working code, not just configure no-code tools.

Understanding of pipeline math and unit economics: you should be able to work backward from a revenue target to SQLs required, conversion rates by stage, and the specific AI interventions that move each number. If someone says "we need 67 SQLs per quarter at 20% win rate and $75K ASD," you should immediately know where AI can compress that funnel.

Experience with AI agent orchestration — managing multiple AI agents working different segments or functions simultaneously, resolving conflicts, preventing duplicate outreach, and maintaining data quality in the hub (Salesforce).

Demonstrated ability to build

AI Go-to-Market Engineer at Flosum