AI Deployment Manager
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Business
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IT/Tech
Job Description & How to Apply Below
We are a technology services company dedicated to helping organizations build and deploy cutting-edge AI solutions. From generative AI and custom LLM integrations to predictive analytics and intelligent automation, we work across industries to bring real-world AI applications to life. Our projects combine deep technical expertise with hands-on client collaboration to solve high-impact problems.
Job SummaryThe AI Deployment Manager works with clients to turn business needs into practical AI solutions and measurable outcomes. We're interested in candidates with depth in either of two areas:
Build, shaping and delivering AI products and workflows with engineering teams; or Enablement, helping people adopt AI through training, hands-on learning, and workflow development with client champions.
You will lead work that fits your strengths and the needs of each engagement. You are not expected to specialize equally in both areas, though experience across both is welcome. In either path, you'll work closely with clients, understand how their teams operate, use AI thoughtfully, and stay accountable for results.
Eliza is AI-native: you will use AI tools across discovery, product definition, workflow building, training, and client communication, and explain where they help and where they fall short. Client work moves quickly and can change as users and evidence reveal new needs; we value thoughtful preparation, adaptability, and reliable follow-through.
Key Responsibilities- 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; make clear recommendations about what is ready to pursue.
- Align client stakeholders on priorities, target users, milestones, and measures of success.
- Translate business needs into product requirements, specifications, acceptance criteria, product roadmaps, and delivery plans engineers can act on.
- 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 toward production.
- Design and lead trainings, workshops, and office hours that help people apply AI in their daily work.
- Co-build, test, and refine prompts, skills, and workflows with client champions; make limitations and review needs clear.
- Turn effective practices into learning materials, curricula, and reusable templates.
- Define success criteria with client experts, including for AI systems that need ongoing evaluation.
- Track product and adoption signals such as usage, realized business outcomes, and time saved; distinguish measured results from early indications.
- Use evaluation results, participant feedback, and adoption evidence to improve the solution and follow-up.
- Manage scope, dependencies, risks, and client cadence while keeping business 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 committee meetings that share progress and shape future opportunities with clients.
- Package proven delivery methods, learning materials, and workflow assets for reuse across engagements.
- Product or delivery experience translating business needs into requirements and coordinating with engineering to move AI or software solutions toward launch.
- Ability to assess feasibility, define acceptance evidence, and manage delivery tradeoffs as prototypes move toward production.
- Experience in client enablement, training, learning and development, adoption, customer success, or change work.
- Ability to facilitate practical learning and help users translate their workflows into AI-supported practices and clear adoption measures.
- Strong AI fluency: practical use of current AI tools and a clear understanding of model strengths, limitations, and safeguards.
- Sound judgment across client needs, business outcomes, technical constraints, and risk; know when to bring in engineering expertise.
- Human-centered…
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