Senior AI Specialist

LVT is looking for a hands-on AI practitioner to own and execute our AI initiatives across the organization. As Senior AI Specialist, you will build and ship production AI systems, shape how LVT adopts AI at scale, and partner cross-functionally to turn AI from an experiment into a competitive advantage. This is an individual contributor role reporting into IT leadership — you’ll work directly with Data Engineering and the Automation Integrations team to build RAG pipelines, develop AI policy, rationalize our AI tool investments, and embed intelligent components within our automation workflows.

This role is based in-office out of our Headquarters in American Fork, Utah.

AI Practitioner First: You have built and shipped LLM-based systems in production — not just used AI-powered SaaS tools. Strategy matters, but your hands are in the code.

Proven AI Depth: You have substantive, hands-on experience building and deploying LLM-based solutions — RAG pipelines, vector databases, agent frameworks, prompt engineering at scale. Using Copilot or ChatGPT at work does not qualify. We need someone who can build the systems, not just use them.

Platform Fluent: You understand enterprise automation platforms (Workato, OIC, or equivalent) and can reason clearly about where AI fits within complex integration flows.

Tech Stack Evaluator: You know how to assess embedded AI features across SaaS and enterprise tools — cutting through vendor marketing to determine what’s actually delivering value, what’s redundant, and what should go.

Policy-Minded: You’ve developed or contributed to AI governance frameworks, usage policies, or organizational AI standards — and you understand the stakes of getting it right.

Cross-Functional Influencer: You build trust with engineering, operations, and executive stakeholders alike, and know how to move initiatives forward without direct authority over every team involved.

Data-Environment Comfortable: You’re experienced working within data platforms (e.g., Snowflake, Cortex) and partnering with data engineering teams to bring AI models from concept to production.

Outcome-Oriented: You measure your work by business impact — adoption rates, efficiency gains, cost reduction — not by the sophistication of the model you shipped.