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описание
The enterprise is going agentic — but most AI agents fail when they hit real business complexity. Shelf is changing that. We’ve built the operating system for agentic AI: a platform that models your policies, workflows, and operational logic into an AI Data Model so agents don’t just respond — they reason. The result? AI that understands how your business actually runs and delivers precise, compliant, auditable outcomes at scale.
задачи
Discover and prioritize AI opportunities. Work with customers to identify valuable AI use cases, balancing business impact with implementation effort, initially focusing on Shelf’s existing customer base.
Define scope and build the business case. Understand the customer’s goals, assess readiness, establish baselines, and agree on measurable outcomes, success criteria, and expected value.
Own implementation. Build and refine the customer’s AI Data Model, including relevant knowledge, entities, relationships, and business rules. Configure Copilots, AI Agents, and workflows in Shelf.
Lead delivery from discovery through rollout. Own plans, milestones, dependencies, risks, and stakeholder decisions. Align customer and internal teams on integrations, access, and deployment requirements.
Validate quality and readiness. Establish evaluation task banks and acceptance criteria, investigate gaps, and verify improvements before expanding deployment.
Drive adoption and customer capability. Guide customers through pilots, collect feedback, and equip their teams to use, operate, and improve the solution.
Prove and expand business impact. Measure results against agreed objectives, communicate value to decision makers, and identify further use cases that support adoption, retention, and expansion.
Make delivery repeatable. Turn implementation experience into reusable playbooks, templates, and automation. Bring structured customer feedback to Product and Engineering.
требования
5+ years in customer-facing B2B SaaS delivery, such as solutions consulting, implementation, professional services, technical customer success, or technical program management.
Proven ownership of complex implementations, from discovery and scoping through deployment and measurable results, involving multiple customer and internal stakeholders.
Hands-on experience implementing AI, GenAI, or automation solutions, including configuring workflows and translating business needs into working systems.
Experience structuring business knowledge, using concepts such as ontologies, taxonomies, entities, relationships, metadata, and business rules.
Technical fluency to work credibly with customer IT and engineering teams on APIs, integrations, authentication, permissions, and data flows.
Strong analytical skills, including defining success metrics, evaluating AI quality, interpreting usage data, and building business cases using SQL or comparable analytics tools.
Clear, consultative communication, with the ability to lead discovery, challenge assumptions, secure stakeholder decisions, and coach customer teams.
Strong ownership in an evolving environment, including prioritizing work, managing risks and dependencies, resolving ambiguity, and improving processes while delivering.
