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

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

› Enablement role building AI fluency across an ad-tech org — you launch training programs, launch an AI Guild, and own adoption metrics, not the platform.

› Enablement 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.

› Builder 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.

› Enablement 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.

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

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

› Trust & 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.

› Operations 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.

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

› Enablement 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.

› Contract eval/annotation role for AI output quality — you judge whether AI-generated content meets accuracy, cultural, and brand standards, with flexible hours and no coding required.

› Entry-level AI ops seat watching dashboards and logs to catch AI failures before they escalate — you flag and coordinate, engineers do the fixing.

› GTM analyst role where you build AI workflows and enable your team to use them — light on dev, heavy on training and adoption.

› Embedded 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.

› Embedding 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.

› No-code AI workflow builder shipping automations across sales, support, and ops — n8n, Make, and Zapier are the main tools, Python is a bonus.

› AI adoption and capability-building seat — you identify AI opportunities across business functions, deploy tools with teams, and build the internal frameworks and playbooks for scaling adoption.

› Enterprise AI adoption role at a major apparel brand distributor — you build rollout programs, literacy training, and use-case pipelines, not production AI systems.

› Support-as-product role where you own the AI workflows that handle tickets — you run escalations, then build the systems that make those escalations.

› Delivery-focused ops role running complex AI implementations inside Fortune 500s — you own program outcomes and client trust, not the engineers doing the building.

› AI adoption role in finance ops — you build learning pathways, playbooks, and champions networks so non-technical employees ship AI prototypes, not production systems.

› Enablement-first AI role in legal tech consulting — you embed in client teams, build integrations and workflows, and drive adoption until AI actually sticks.

› Customer success role owning AI adoption and capability building across enterprise accounts — you train champions and run workshops, not build product.

› Enablement-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.

› Hands-on AI builder seat in GTM ops — you ship AI-powered workflows and tools in days, not weeks, using agentic coding tools.

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

› Revenue systems lead role that builds and runs AI-powered automations with Clay, Zapier, and n8n — you're owning the GTM tech stack and its workflows, not just advising on it.

› Enablement role driving AI adoption across Jefferies' Equity Research team — heavy on training, hands-on coaching, and low-code tooling, light on dev work.

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

› Applied AI builder role shipping LLM features, evals, routing, and automation from business need to working prototype.

› Automation role building agentic workflows across marketing, CRM, and handoffs, with fallbacks and state logic.

› Internal enablement role training teams, finding use cases, and prototyping agent workflows with enough technical depth to be credible.

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

› Deploys agents that clean messy retail catalog data, turning ambiguous product information into working sales workflows.

› Entry-level role building voice agents through conversation design, prompt engineering, and agent behavior translation.

› Customer-facing agent PM role owning prompt logic, agent design, rollout, and roadmap advice for enterprise deployments.

› Internal AI ops role building tools, prompt libraries, SOPs, and adoption loops across company functions.

› Owns revenue agents like teammates, with scope, metrics, guardrails, and ongoing tuning across GTM workflows.

› Conversation design role writing prompts, optimizing chatbot flows, and QA-testing LLM experiences for customer journeys.

› Evaluation role owning RAG and agent quality frameworks, with research-grade Python instead of production infrastructure.

› Healthcare QA role reviewing AI agent calls, catching errors, labeling issues, and improving real workflows.

› Operator seat embedded in revenue, platform, or research work, using frontier AI to tighten execution rhythms.

› Owns production chatbots and AI agents, refining prompts and workflows for support, onboarding, and troubleshooting.

› Owns Claude-powered CX automations, from use-case selection and prompt frameworks to performance monitoring.

› Enablement role teaching client teams to keep working with AI through workshops, coaching, plugins, and reusable playbooks.

› Builds and operates AI workflows for GTM teams, combining agents, automation, systems integrations, and human review.

› Builds LLM workflows and leaves teams able to run them, prompt engineering and agent debugging included.

› Consultant role building AI playbooks, competency rubrics, and adoption dashboards for GTM and ops teams.