AI GTM Lead

Cloudious•Phoenix, AZ

About The Position

The AI GTM Lead drives enterprise adoption and delivery of generative AI and agentic AI capabilities. The role combines go-to-market leadership with forward-deployed engineering: defining adoption strategies for AI platforms and working directly with business teams to prototype, build, and deploy AI-powered solutions in real workflows. The position partners closely with product, engineering, and line-of-business stakeholders to turn emerging AI capabilities into business impact and accelerate experimentation, deployment, and scale. It calls for a strong bias for action, comfort with ambiguity, and effective use of modern AI-native development tools to move rapidly from ideas to working solutions. The role leads adoption strategies and platform launches; builds prototypes, demonstrations, and reference implementations; enables developers through workshops and reusable assets; and translates business needs into GenAI and agentic architectures. It also works with platform teams on reusable patterns and reference architectures, gathers feedback from end users to influence the product roadmap, and promotes a customer-first, experimentation-driven culture. The provided description ends mid-section at “Act as a trusted advisor,” so any further stakeholder engagement details are not available.

Responsibilities

Define and execute go-to-market strategies for enterprise AI capabilities, particularly LLMs, agentic systems, and composable AI platforms.
Drive adoption of APIs, agent frameworks, orchestration layers, and developer tools through targeted enablement and engagement.
Lead feature launches and platform rollouts, translating technical capabilities into clear business-value narratives.
Partner with business and engineering teams to design, prototype, and deploy generative AI and agentic solutions.
Build hands-on demonstrations, reference implementations, and rapid prototypes for real-world use cases.
Engage in pair programming, debugging, and solution development to accelerate progress from concept to production.
Use AI-assisted development platforms to solve problems and iterate rapidly.
Lead workshops, office hours, and hands-on sessions on LLMs, RAG architecture, and agentic workflows.
Develop scalable enablement assets such as SDKs, playbooks, reusable components, prompt libraries, and agent templates.
Identify developer-experience friction points and drive improvements to tooling and documentation to improve time-to-value.
Translate business problems into GenAI and agentic solution architectures using patterns such as RAG, tool use, multi-agent orchestration, and memory frameworks.
Partner with platform teams to define reusable design patterns, accelerators, and reference architectures.
Stay current with GenAI, agentic systems, LLMOps, and emerging AI-native development paradigms.
Establish feedback loops with end users to shape the platform roadmap and prioritize enhancements.
Capture insights from deployments to inform improvements in usability, performance, and scalability.
Champion a customer-first, experimentation-driven culture across AI initiatives.