AI Deployment Manager

Role overview Guide client organizations from AI opportunities to practical solutions and measurable results. The role can lean toward building: translating business needs into requirements and coordinating delivery with engineers; or enablement: helping teams learn, adopt, and improve AI-supported workflows. Across either path, you’ll collaborate with clients, stay accountable for outcomes, and adapt as evidence and user needs evolve.

Responsibilities - Lead conversations with stakeholders and users to understand goals, workflows, data readiness, pain points, and barriers to adoption. - Assess candidate AI use cases for feasibility, risk, timing, and potential value, then align stakeholders on priorities and success measures. - For build-focused work, shape requirements, acceptance criteria, roadmaps, and delivery plans; partner with engineers through evaluation, launch, and handoff. - For enablement-focused work, run practical training and workshops, develop and refine workflows with client champions, and create reusable learning materials. - Track product and adoption signals, including usage, business outcomes, and time saved, using evaluation and feedback to improve the work. - Manage scope, dependencies, risks, and stakeholder communications; share progress with executives and package proven methods for reuse.

Requirements - Meaningful experience in at least one area: product or delivery work translating business needs into software or AI requirements, or client training, adoption, customer success, or change work. - For build-focused candidates, ability to assess feasibility, define acceptance evidence, and manage tradeoffs as prototypes move toward production. - For enablement-focused candidates, ability to facilitate practical learning and help users apply AI to their work with clear adoption measures. - Practical fluency with current AI tools, including awareness of model limits and safeguards. - Sound judgment across client goals, technical constraints, outcomes, and risk, with the ability to involve engineering expertise when needed. - Strong facilitation, executive communication, concise writing, organization, and follow-through.

Nice to have - Experience spanning both product delivery and enablement, an MBA, or hands-on work developing AI prompts and workflows with enterprise teams. - Background in adult learning, curriculum design, adoption measurement, or software delivery practices such as backlogs and sprints. - Experience in finance, healthcare, or another complex industry.

Benefits and work setup - Compensation includes base pay and performance incentives tied to client outcomes, plus equity options. - Work spans a range of industries and client AI problems; on-site travel may be available for those who want it.