You are an applied AI engineer whose product is the revenue engine. You find where the customer lifecycle can work better and ship production-grade agentic workflow, LLM pipelines and integrations that make each process best in class for reps, CSMs and marketers. You operate independently and own each build end to end: architecture, prototype, evals, deployment and observability. You spar on solution design with the team, then ship.
We care more about what you have shipped than where you studied. If you have built something real, show us.
Must-haves
You have shipped AI systems GTM teams use daily. Agents, workflows or integrations used by sales, CS or marketing that moved a number.
Strong integration skills in JavaScript or TypeScript. You’re comfortable building and running integration services. Working with APIs and webhooks, handling auth, rate limits and errors, and shipping tested code.
Hands-on Salesforce or equivalent, declarative and code. Declarative: object model, Flows, validation rules, permission sets, record types. Code: Apex triggers and classes, SOQL and SOSL, Lightning Web Components, Platform Events and Change Data Capture, REST and Bulk APIs, SFDX deployments.
Applied AI engineering. You build with LLM APIs, not just chat interfaces, and can show the skills listed below in real work.
You understand the revenue engine. Experience in RevOps, GTM Systems, growth or a GTM engineering agency in B2B SaaS. You know how pipeline, renewals and expansion actually work.
Data quality fundamentals. SQL, CRM data models, deduplication, enrichment waterfalls and data quality at scale.
GTM data stack. Clay, n8n, Make + warehouse and reverse ETL experience.
Production mindset. You ship fast and build things others can maintain: logged, monitored, documented.
Clear communicator. You can sit with a top rep, find the work that shouldn't exist, and explain the fix in plain words.
Nice-to-haves
Experience with a customer platform or CSM tool (Planhat, Gainsight, ChurnZero), or HubSpot alongside Salesforce.
Salesforce certifications such as Platform Developer I or II, or Platform App Builder.
Side projects, open-source tools, public write-ups or a GitHub that shows you build for fun.
Founder, agency or early-stage startup experience.
You’ll own each build end to end, from architecture and prototype through evals, deployment and observability. Here’s what that looks like:
Agentic GTM workflows: Design and ship AI agents and workflows that optimise productivity: account research, signal detection, enrichment, call and email analysis, personalised outreach, renewal and expansion plays.
Planhat on Planhat: Run our own GTM on our own platform. Build with Planhat's AI, automations and APIs, and feed what you learn straight back to Product as a power user.
Signals and data: Turn product usage, intent, firmographic and conversation data into scores and triggers that route the right account to the right person at the right time.
Integrations: Connect tools that don't talk natively. Build the webhooks, MCP connection and transformations that keep data clean across the stack.
Build vs buy: Run tight pilots on new GTM and AI tools, measure fit and ROI, and make a clear call.
Enablement: Document what you build, teach teams to use it with confidence, and raise how the whole company builds with AI.
Success is measured in outcomes: productivity gains, pipeline acceleration, data quality and adoption of what you ship.
