Live roles

Editor’s noteSenior IC driving enterprise-wide AI fluency and workflow redesign across people teams — builds enablement programs, not production software.

Editor’s noteBuild and ship AI-powered workflows across sales, marketing, support, HR, and finance — you own the automation end-to-end, measured by what ships, not what's scoped.

Editor’s notePM role shipping LLM-powered features (agents, RAG, intelligent assistants) on a sustainability platform — vibe-code your own prototypes, set eval criteria, and own what gets built.

Editor’s noteAdoption role at an energy company scaling AI tools across teams — you drive capability, not code; light on dev, heavy on coaching and rollout.

Editor’s noteInternal AI enablement role getting Microsoft Copilot and M365 AI tools actually used by operations staff — heavy on training, coaching, and workflow translation, light on dev.

Editor’s noteOwns AI support infrastructure from the ground up — you deploy agents, build the knowledge base that retrieves against, then automate ticket flows so the team stays lean.

Editor’s noteBuild clinical AI modules in a no-code platform for hospital workflows — prompt work, validation against clinical evidence, light on traditional engineering.

Editor’s noteInternal AI transformation lead role — you own how the whole company adopts AI, from hands-on prototyping with RAG and lightweight agents to driving adoption across GTM, Services, and Finance.

Editor’s noteEnablement-focused AI role building workflows and training GTM teams to use AI tools — you ship the adoption, not the platform.

Editor’s noteCoaching and change-management role in construction that gets employees actually using AI tools — no AI system building required, heavy on 1:1 enablement and training delivery.

Editor’s noteSenior architect role shipping AI solutions for government and enterprise clients using vibe coding and agentic IDEs — you build the proof-of-concept, not the platform.

Editor’s noteProduct Builder role defining agent analytics as a category—you prototype with AI tools, ship working software, and own strategy end-to-end, not just specs and handoffs.

Editor’s noteImplementation-focused CSM role where you deploy Glean's AI platform for enterprise customers — heavy on project management, SSO/connector setup, and adoption monitoring.

Editor’s noteOperations-focused AI role owning end-to-end agentic workflows in production — you monitor, tune, and iterate the workflows, not the platform underneath.

Editor’s noteAI-native PM role — you own 0-to-1 product building end-to-end using vibe-coding tools, then hand off to engineering for scaling.

Editor’s noteEnablement role connecting AI capabilities to teams — you build and deploy MCP tool bundles, then measure success by whether teams actually use what you ship.

Editor’s noteEnablement role building AI fluency inside a marketing insights team — you own the AI playbook, run pilots, and make the team more capable, not the AI systems themselves.

Editor’s noteIT support role focused on troubleshooting AI tools like Copilot, Glean, and ChatGPT across a financial consulting firm — you're the escalation point, not the builder.

Editor’s noteAI adoption role embedded in construction field ops — you coach jobsite teams on AI tools, translate capabilities into construction language, and feed field intelligence back into what gets built next.

Editor’s noteOps role running AI workflows in a post-sales org — you build automations in Workato and configure them in Salesforce/Gainsight/Zendesk, not write production code.

Editor’s noteOwn the Self-Service Ecosystem end-to-end — prototype AI workflows, tune knowledge bases for LLM retrieval, and measure deflection. You're the bridge between support ops and AI tooling, not a platform engineer.

Editor’s noteOps seat building and owning AI workflows that automate executive operations — you ship production agents weekly, not platforms or products.

Editor’s notePM seat owning agent behavior end-to-end — you define how AI agents reason, interpret intent, and execute workflows, then measure whether they actually work.

Editor’s noteEnablement seat embedding AI capability across an investment firm's business units — you build teams' AI skills and reusable patterns, not the platform itself.

Editor’s noteRuns and quality-checks AI-driven medical workflows for US clinics — you own the outputs, tweak prompts, and escalate to engineers when needed, not when you can fix it yourself.

Editor’s noteTrainer role at Dr. Ng's advisory firm — you translate deep AI concepts (RAG, evals, agents) into executive-ready workshops and curriculum, light on coding, heavy on teaching.

Editor’s noteForce-multiplier ops role applying AI tools like Zapier and ChatGPT to automate internal workflows at a science-AI startup — you own operational systems, not the AI platform itself.

Editor’s noteHands-on AI ops role running and scaling Ramp's customer-facing agentic workflows — you ship the automation and own how it performs week-to-week, not build the platform.

Editor’s noteCustomer-facing adoption role — you help enterprises actually use Writer's AI tools at scale through training, playbooks, and workflow mapping, not build the platform itself.

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 noteEnablement seat translating attorney needs into AI-assisted workflows — you train, support, and maintain the use-case library, not the infrastructure.

Editor’s noteStrategic AI implementation role at a finance consulting firm — you build internal AI tools and prompt libraries, then turn that work into playbooks and SOPs so the whole firm can actually use AI at scale.

Editor’s noteConsulting role getting AI actually used inside German Mittelstand companies — you ship enablement and training, not code, with a real consulting framework backing your work.

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 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 noteHands-on AI ops role building and running automation workflows in marketing with Zapier/Make — you ship the automation, not the platform.

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 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 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 noteConfigures LLM support agents through intent systems, routing logic, retrieval setup, and evaluation loops.

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

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

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

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.