Live roles

Editor’s noteEmbedded AI enablement role — you build agentic workflows with n8n/Zapier, coach teams on adoption, and own making sure tools actually get used.

Editor’s noteOperations role building and tuning AI workflows with Make, Airtable, and LLMs — you ship the automation, not the platform.

Editor’s noteStartup-in-a-corp unit for live commerce — vibe code 48-hour prototypes and agentic tools, then ship to production with continuous iteration.

Editor’s noteAnalyst advisory role on agentic development and AI-native tooling — you translate market trends into client guidance, not build or operate AI systems yourself.

Editor’s noteInternship embedding AI adoption across business teams — you train, build playbooks, and translate tools into daily habits for non-technical colleagues. Light on dev.

Editor’s noteEarly-career ops seat building and managing AI agents for marketing campaigns — you ship automation across B2C/B2B workflows, light on dev for a product builder.

Editor’s noteInternal AI product builder role — you ship tools and automations that ~180 people actually use, with AI as your primary building instrument rather than deep engineering.

Editor’s noteGTM ops role that builds and runs AI-powered workflows with Clay, HubSpot, and custom agents — you ship the automation systems, not the product platform.

Editor’s noteInternship building internal AI agents for product ops using vibe coding tools — you're the architect of the workflow and the team's AI translator.

Editor’s noteEnablement lead role building AI capability across a 10-department org — you own the champion network, skills framework, and training program, with light prototyping when teams get stuck.

Editor’s noteBuilder seat in GTM Operations — you ship AI workflows and agents into production, monitor how they run, and iterate fast. Light on dev, heavy on connecting business needs to working automation.

Editor’s noteEnablement seat getting AI tools actually adopted across teams — you audit workflows, build training, and translate AI for non-technical folks; light on building, partner with tech team for custom solutions.

Editor’s noteEnabling business teams to actually use AI — design sprints, training, and prototyping that transfers capability, not production infrastructure. You build to teach, not to own.

Editor’s noteTrust & Safety role building and running AI workflows that flag content, route reports, and handle escalations — you're measured on automation rate and handle time, not on shipping features.

Editor’s noteOperations role configuring and improving Mercury’s customer-facing chatbot and email agents — you monitor performance, expand bot scope, and optimize workflows rather than build the underlying platform.

Editor’s noteStaff-level AI agent PM seat owning enterprise agent behavior and requirements — you define what agents do, not what infrastructure they run on.

Editor’s noteEnablement role bridging AI tools and construction teams — you spend 60-80% on jobsites, coaching PMs and superintendents to actually use the tools, not building the tools themselves. Light on dev, heavy on field work and adoption.

Editor’s noteEmbedded AI enablement role in construction tech — you build Claude skills and automations for ops, finance, and sales, then train those teams to own the work after you ship it.

Editor’s noteEmbedding IC role that builds and enables AI capability across business teams — you reimagine workflows with agents and train teams to own them, not hand off to engineering.

Editor’s noteNo-code AI workflow builder shipping automations across sales, support, and ops — n8n, Make, and Zapier are the main tools, Python is a bonus.

Editor’s noteSupport-as-product role where you own the AI workflows that handle tickets — you run escalations, then build the systems that make those escalations.

Editor’s noteEnablement-focused PM role — you're the AI champion teaching non-technical staff, building training programs, and managing rollout, not engineering AI systems. Light prototyping with Replit/Lovable OK, deep coding not expected.

Editor’s noteHands-on AI builder seat in GTM ops — you ship AI-powered workflows and tools in days, not weeks, using agentic coding tools.

Editor’s noteCustomer-facing AI adoption role helping enterprise clients translate workflows into agentic outcomes — heavy on workshops, prompting, and rollout, light on writing production code.

Editor’s noteRapid-fire builder role using AI coding tools to ship functional code and internal tools — you're generating PRs, not just prototyping.

Editor’s noteApplied AI builder role shipping LLM features, evals, routing, and automation from business need to working prototype.

Editor’s noteAutomation role building agentic workflows across marketing, CRM, and handoffs, with fallbacks and state logic.

Editor’s noteInternal enablement role training teams, finding use cases, and prototyping agent workflows with enough technical depth to be credible.

Editor’s noteDeploys agents that clean messy retail catalog data, turning ambiguous product information into working sales workflows.

Editor’s noteEntry-level role building voice agents through conversation design, prompt engineering, and agent behavior translation.

Editor’s noteCustomer-facing agent PM role owning prompt logic, agent design, rollout, and roadmap advice for enterprise deployments.

Editor’s noteInternal AI ops role building tools, prompt libraries, SOPs, and adoption loops across company functions.

Editor’s noteOwns revenue agents like teammates, with scope, metrics, guardrails, and ongoing tuning across GTM workflows.

Editor’s noteEvaluation role owning RAG and agent quality frameworks, with research-grade Python instead of production infrastructure.

Editor’s noteHealthcare QA role reviewing AI agent calls, catching errors, labeling issues, and improving real workflows.

Editor’s noteOperator seat embedded in revenue, platform, or research work, using frontier AI to tighten execution rhythms.

Editor’s noteOwns production chatbots and AI agents, refining prompts and workflows for support, onboarding, and troubleshooting.

Editor’s noteOwns Claude-powered CX automations, from use-case selection and prompt frameworks to performance monitoring.

Editor’s noteEnablement role teaching client teams to keep working with AI through workshops, coaching, plugins, and reusable playbooks.

Editor’s noteBuilds and operates AI workflows for GTM teams, combining agents, automation, systems integrations, and human review.

Editor’s noteBuilds LLM workflows and leaves teams able to run them, prompt engineering and agent debugging included.

Editor’s noteConsultant role building AI playbooks, competency rubrics, and adoption dashboards for GTM and ops teams.

Editor’s noteCX AI role turning service workflows into intents, escalation logic, knowledge systems, and voice/chat automation.

Editor’s noteQA role reviewing AI support conversations for accuracy, policy fit, tone, and recurring failure patterns.

Editor’s noteDaily agent ops role building, monitoring, debugging, and tuning conversational AI workflows across enterprise use cases.

Editor’s noteShapes AI personality, tone, prompts, and guardrails for conversational systems, more behavior design than flowchart work.

Editor’s noteBuilds and runs AI automations with Claude, n8n, and similar tools, shipping practical workflow fixes fast.

Editor’s noteBuilds internal AI capability through champions, workflow-mapping workshops, and training for no-code agent platforms.

Editor’s noteAI product role taking agent features from concept to launch, with workflow tuning and interface design in the mix.