AI Specialist
LVT Corp
ABOUT LVT LVT is redefining how businesses operate in the physical world, moving beyond traditional security solutions to deliver AI-driven, actionable intelligence that makes sites smarter, safer, and more secure. Since pioneering our first mobile, solar-powered units, our commitment to scrappy, hands-on innovation has made us an established leader and one of the fastest-growing companies in intelligent site technology. We are building the next generation of solutions-from our physical units in the field to a powerful Agentic AI platform-that allows our customers to gain unprecedented visibility and control over safety, compliance, and operations. This is your chance to join a cutting-edge team that isn't just watching the world change, but actively building the technology that is changing it. We're a team that's focused on growth and innovation, and we're proud that our crew, products, and leadership are being recognized for it.
ROLE RESPONSIBILITIES
- A Top-Tier Growth Company: Named one of the Financial Times' Fastest Growing Companies 2025 and #10 on the Inc. 5000 Rocky Mountain Regional list for 2025.
- Innovative Leadership: Our CEO, Ryan Porter, was named an EY Entrepreneur of the Year 2025, and our CTO, Steve Lindsey, was inducted into the Silicon Slopes CTO Hall of Fame in 2024.
- Product & Software Excellence: We were named one of The Software Report's Top 100 Software Companies of 2023 and are a winner of the Security Today Govies Award for 2025.
ROLE RESPONSIBILITIES
- Execute LVT's AI adoption strategy, owning the roadmap of AI initiatives and driving measurable business value across departments.
- Develop and own LVT's internal AI policy framework, setting standards for responsible AI use, governance, and organizational readiness.
- Audit and rationalize the AI capabilities embedded across LVT's existing tech stack - driving adoption of high-value tools, consolidating redundancies, and deprecating underutilized spend.
- Build and deploy RAG pipelines and Cortex-based models in partnership with Data Engineering, translating business needs into production-ready AI solutions.
- Architect the AI strategy at the intersection of automation - defining where and how AI augments workflows built on platforms like Workato and Oracle Integration Cloud (OIC).
- Design and build the AI components embedded within automation flows, partnering with the Automation Integrations team to ensure seamless, intelligent execution.
- Champion AI literacy and adoption across LVT, partnering with business leaders to identify high-impact opportunities and drive measurable outcomes.
- Communicate AI strategy and progress to executive leadership, translating technical direction into clear business outcomes.
- 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.
Vacancy posted 16 hours ago
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