AI Deployment Manager
Eliza · United States
About this role
## AI Deployment Manager ### About Us We’re a technology services company helping organizations build and deploy cutting-edge AI solutions—spanning generative AI, custom LLM integrations, predictive analytics, and intelligent automation. We combine deep technical expertise with hands-on client collaboration to deliver real-world AI applications. ### Job Summary The **AI Deployment Manager** partners with clients to turn business needs into practical AI solutions with measurable outcomes. You’ll lead work that matches your strengths in one (or both) of these areas: - **Build**: Shape and deliver AI products and workflows with engineering teams. - **Enablement**: Help people adopt AI through training, hands-on learning, and workflow development with client champions. You’ll work closely with clients to understand how their teams operate, use AI thoughtfully, and stay accountable for results. Client work moves quickly and may change as users and evidence reveal new needs—so we value preparation, adaptability, and reliable follow-through. ### Key Responsibilities 1. **Discover client needs and define the opportunity** - Lead stakeholder and user conversations to understand business goals, workflows, pain points, data readiness, and adoption barriers. - Assess AI use-case fit, feasibility, risk, timing, and potential impact; recommend what’s ready to pursue. - Align stakeholders on priorities, target users, milestones, and measures of success. 2. **Build: shape and deliver AI products and workflows** - Translate business needs into product requirements, specifications, acceptance criteria, roadmaps, and delivery plans. - Partner with engineers through design, evaluation, iteration, launch, and handoff (engineering owns implementation and infrastructure decisions). - Surface edge cases, data quality, compliance, and scalability risks as work moves from prototype to production. 3. **Enablement: help client teams adopt AI** - Design and lead trainings, workshops, and office hours to help people apply AI in daily work. - Co-build, test, and refine prompts, skills, and workflows with client champions; clearly communicate limitations and review needs. - Turn effective practices into learning materials, curricula, and reusable templates. 4. **Measure outcomes and improve the work** - Define success criteria with client experts, including for AI systems requiring ongoing evaluation. - Track adoption and product signals (usage, realized business outcomes, time saved), distinguishing measured results from early indications. - Use evaluation results, participant feedback, and adoption evidence to improve the solution and follow-up. 5. **Align stakeholders and scale what works** - Manage scope, dependencies, risks, and client cadence while keeping stakeholders, subject-matter experts, and technical teams aligned. - Communicate progress, decisions, and tradeoffs clearly; raise feasibility concerns and challenge low-value requests early. - Prepare and facilitate executive steering (as needed).
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