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Agent Systems Engineer is what we call this role. You may know it as GTM Engineer, Revenue Systems Engineer, Growth Engineer, or internal Forward Deployed Engineer. The substance is the same. You're a deeply technical operator who embeds with revenue teams, learns how the business actually moves, and rebuilds workflows as governed, AI-augmented systems.

You write SQL fluently. You live in APIs. You've shipped production GTM automation that other people depend on. You know what good revenue data architecture looks like (warehouse as source of truth, modeled in dbt, activated into Salesforce and the rest of the stack via Reverse ETL) and you have opinions about why most companies get it wrong.

You also believe in practical AI. You've deployed LLMs against real GTM problems where the business value was concrete (account research, classification, enrichment, content generation), and you have honest views about what worked, what didn't, and where the hype outpaces reality.

Most companies treating AI as a productivity tool are pointing it at individual jobs. We think the bigger opportunity is rebuilding entire revenue processes around agents and modern data infrastructure. We sell that thesis to product security teams every day. Running our own GTM motion the same way is how we hold ourselves to the standard we're selling.

You'd be first in seat at Finite State, so you own the full motion: discovery with revenue leaders, system design, build, governance, evaluation, rollout, and the runbook so it survives you.

• Run discovery with revenue leaders before building. Sit in pipeline reviews. Watch a deal cycle end-to-end. Find the actual time sinks before designing a solution
• Architect the GTM data layer: Snowflake (or equivalent) as source of truth, dbt for modeling, Reverse ETL (Hightouch, Census) for activation into Salesforce, HubSpot, Outreach, and the rest of the stack
• Design and build AI agents and AI-augmented workflows for revenue-critical work: account research, ICP scoring, signal-based plays, outbound personalization, CRM enrichment, deal intelligence, churn risk, expansion triggers, lead routing
• Deploy LLMs and agents where they add real business value, and skip them where they don't. We're not interested in AI for the sake of AI
• Wire agents and systems together via APIs, webhooks, MCP servers, and lightweight code (Python, SQL, TypeScript). Use platforms like Clay, n8n, Workato, or Hightouch AI when they fit. Build custom when they don't
• Build signal pipelines that capture buying intent (hiring patterns, funding events, security disclosures, product telemetry from our own platform) and trigger the right agent or action automatically
• Stand up the governance layer for every agent you ship: permissions, audit trails, access controls, sensitive data handling, and rollback paths
• Build evaluation harnesses that measure real business outcomes (pipeline generated, deals accelerated, rep hours saved), not just whether the agent ran
• Codify recurring patterns as reusable skills so the next agent doesn't start from scratch
• Document the architecture and write the runbook so the next person on the team can learn from your work
• Expand into adjacent functions (Finance, People, Security ops) as the pattern proves out

Required

• 5+ years in RevOps, Growth Ops, GTM Engineering, Sales Engineering, or Solutions Engineering, with production work that other people relied on
• Strong technical chops: fluent SQL, comfortable in Python or TypeScript, lives in APIs and webhooks, reasons cleanly about data flow and auth
• Modern data stack experience in production: warehouse (Snowflake, BigQuery), transformation (dbt), Reverse ETL (Hightouch, Census). You've shipped this, not just read about it
• Deep Salesforce or HubSpot. Custom objects, schema design, sync logic, the limits and workarounds. You have battle scars
• Working knowledge of the modern GTM stack: Outreach or Salesloft, Gong, ZoomInfo, Clay, Apollo, LinkedIn Sales Navigator, product analytics
• Production experience deploying LLMs and AI agents in GTM workflows. You don't need to have built agent frameworks from scratch. You do need to have shipped something real and have informed views about what worked
• Discovery instincts. You sit with the people doing the work before building. You ask the right questions and find the actual problem
• Process thinking. You map full workflows including the messy human handoffs and have opinions about what should stay human
• Judgment about revenue work. You can tell the difference between something that drives pipeline and something that just looks good in a dashboard
• Strong sense for security, governance, and risk. This matters double at a product security company touching customer and prospect data
• Self-directed. You can run a stakeholder conversation, define the process, and ship a v1 without a PM translating for you

Nice to have

• Reverse ETL, CDP, or growth platform experience (Hightouch, Census, Segment, Rudderstack)
• Hands-on experience with modern agent frameworks, MCP servers, evals, or current-generation agent SDKs
• Prior work supporting a PLG motion or a sales-led-to-PLG transition
• Public writing about your work (blog, Substack, talks). We value people who can explain their thinking
• Familiarity with security buyer personas (CISOs, product security leaders, PSIRT teams)

GTM teams across the industry are racing to bolt AI onto individual reps. We think the real unlock is engineering the revenue motion itself, with agents at the core and modern data infrastructure underneath. The people who can do that work, spanning technical depth, modern data fluency, and revenue process judgment, will be valuable for a long time.

You'd be the first inside Finite State.

• $192,000 - $230,000