AI Transformation Lead

Verathon is a global medical device company focused on supporting customers by being their trusted partner, delivering high-quality products that endure over time and ensure clinical and economic utility.

Two areas where Verathon has significantly impacted patient care, and become the market leader in each, are bladder volume measurement and airway management.

The company’s BladderScan portable ultrasound and GlideScope video laryngoscopy & bronchoscopy systems effectively address unmet needs for healthcare providers and meaningfully raise the standard of care for patients.

Verathon, a subsidiary of Roper Technologies, is headquartered in Bothell, Washington, USA and has international subsidiaries in Canada, Europe and Asia Pacific.

For more information, please visit www.verathon.com .

At Verathon, we are accelerating the responsible use of artificial intelligence to transform how we work, innovate, and serve customers.

The AI Transformation Lead plays a critical role in this journey by connecting business needs with AI solutions, enabling teams to realize measurable value, and helping build the in-house capabilities that make AI reflexive across the company.

This position is ideal for a business-savvy problem solver who understands both operational processes and emerging AI technologies and who thrives in a collaborative, cross-functional environment.

The role includes leading Verathon’s business-side context engineering, shaping the domain knowledge, workflows, and instructions that enable AI systems to perform reliably.

• Partner with business and functional leaders to identify, prioritize, and scope high-value AI opportunities across R&D, Commercial, Operations, and G&A functions.
• Analyze end-to-end business processes to uncover inefficiencies and define where AI can automate, augment, or reimagine the work.
• Translate business challenges into practical AI solution requirements, collaborating closely with IT to assess feasibility and integration needs. Ensure business context is accurately translated into prompt scaffolding, agent logic, and RAG configurations.
• Define and structure the operational and domain context required for AI solutions, including instructions, workflows, edge cases, and success criteria (“context engineering”).
• Lead and track pilot initiatives, ensuring measurable outcomes and lessons learned are captured for scaling.