GTM AI & Systems Builder

Overview

You build AI-powered tools and automations for Confluent's GTM organization (sales, RevOps, CSM, etc.). The company has already invested in the AI stack - your job is to ship working solutions that 1,500+ revenue people will actually use. You report to a Senior Manager who runs GTM AI & Systems, so you're close to the decision-making but focused on execution.

Role Snapshot

Company Context

Stage: Public (IPO'd in 2021)

Size: ~3,700 employees

Growth: Mature SaaS company, original creators of Apache Kafka, cloud-native data streaming platform

Market Position: Category leader in data streaming, competing against AWS Kinesis, Azure Event Hubs, and open-source Kafka implementations

GTM Reality

The GTM Org You're Supporting:

• 1,500+ users across sales, SDRs, AMs, CSMs, SEs, and RevOps
• Selling complex infrastructure software ($100K+ ACVs) with 3-9 month sales cycles
• Mix of enterprise AEs, mid-market teams, and product-led growth motions
• Heavily technical sale - data engineers and architects are key buyers

Your Manager's Focus:

• Senior Manager of GTM AI & Systems at Confluent
• Owns the AI/automation strategy for the entire revenue org
• Already has budget and buy-in - now needs execution

What You'll Actually Do

Time Breakdown

Building/Coding (50%) | Stakeholder Mgmt (25%) | Testing/Iteration (25%)

Key Activities

• Build AI agents and automations: You're writing code (Python, API integrations, prompt engineering) to create tools that help reps research accounts, draft emails, analyze deal health, or surface insights. Think Claude/GPT wrappers, Zapier/Make.com workflows, or custom internal apps.
• Ship and iterate based on feedback: You launch something, watch how 10-50 people use it, then fix what breaks or doesn't work. Lots of Slack DMs asking "why isn't this working for my territory?" or "can you make it do X instead?"
• Integrate with the existing stack: Confluent already has Salesforce, Gong, Outreach, ZoomInfo, etc. You're connecting AI tools to these systems - pulling data, pushing updates, triggering workflows. Expect a lot of API documentation reading.
• Internal demos and enablement: Once something works, you show it to teams in Slack/Zoom, write quick docs, and handle the "how do I use this?" questions. Not formal training - more like office hours and async Loom videos.

The Honest Reality

What's Hard

• Adoption is your biggest challenge: You can build the perfect tool, but if reps don't use it (because they're busy, skeptical, or it's slightly annoying), it doesn't matter. You'll spend a lot of time figuring out why usage dropped off.
• Everyone has opinions, few have answers: RevOps will say "make it pull X data," sales managers will say "my team needs Y," and your manager wants Z shipped by end of quarter. You have to triage and sometimes just pick a direction.
• The AI stuff breaks in weird ways: Prompts that worked yesterday suddenly return garbage. APIs rate-limit you. Salesforce changes a field name. You're constantly debugging edge cases and user-reported issues.
• You're building for a skeptical audience: Sales reps have seen a dozen "AI tools" that were just chatbots. They'll try your thing once, and if it wastes their time, they're done. First impression is everything.

What Success Looks Like

• You ship 3-5 tools in your first 6 months that get regular usage (20%+ of target users engage weekly)
• Reps start asking you for features instead of complaining about what doesn't work
• Your manager can point to clear time savings or process improvements in quarterly reviews
• Other teams (marketing, product) start asking if you can build stuff for them

Who You're Building For

Primary Users:

• SDRs/BDRs: Need help with account research, email personalization, daily activity tracking
• AEs: Want deal insights, competitive intel, proposal drafting assistance
• SEs: Looking for demo prep, technical response generation, POC automation
• RevOps: Need dashboards, forecasting tools, data cleanup automation

What They Care About:

• Does it save time or make me more effective? If your tool adds 3 steps to their workflow, they won't use it.
• Is it accurate enough to trust? They can't send AI-generated emails that sound like a robot or have wrong company names.
• Does it integrate with what I already use? If they have to leave Salesforce or Outreach to use your tool, adoption dies.

Requirements

• You've actually built and shipped AI tools/automations before (not just theorized about it) - the post specifically asks for "something you've built & what it's changed"
• You understand how revenue teams work day-to-day: what SDRs do vs AEs, what metrics they're measured on, where they waste time
• Strong technical background: RevOps, consulting, product management, or software engineering. You need to code or at least deeply understand how systems connect.
• Self-directed and opinionated: There's no PRD or backlog here. You need to talk to users, figure out what to build, and ship it without a lot of hand-holding.
• Business acumen: You can talk to a VP of Sales about pipeline conversion and also debug a Python script - that combination is rare and critical here.
• Based in the US and comfortable remote: You're working across time zones with GTM teams, so you need overlap with US business hours.