AI Infrastructure Engineer, LifeMD, NY, US

Job Description

About us

LifeMD is a leading digital healthcare company committed to expanding access to virtual care, pharmacy services, and diagnostics by making them more affordable and convenient for all. Focused on both treatment and prevention, our unique care model is designed to optimize the patient experience and improve outcomes across more than 200 health concerns. 

To support our expanding patient base, LifeMD leverages a vertically-integrated, proprietary digital care platform, a 50-state affiliated medical group, a 22,500-square-foot affiliated pharmacy, and a U.S.-based patient care center. Our company — with offices in New York City; Greenville, SC; and Huntington Beach, CA — is powered by a dynamic team of passionate professionals. From clinicians and technologists to creatives and analysts, we're united by a shared mission to revolutionize healthcare. Employees enjoy a collaborative and inclusive work environment, hybrid work culture, and numerous opportunities for growth. Want your work to matter? Join us in building a future of accessible, innovative, and compassionate care.

About the role

We're looking for an AI Infrastructure Engineer to own and maintain our AI infrastructure on GCP, which powers our voice assistants, message routing, and knowledge retrieval systems. This role will manage the technical backbone that enables our AI services to operate safely and effectively in healthcare settings. You will be the technical steward of our AI operations infrastructure and a partner to clinical and product stakeholders. Your job is to build new AI capabilities, maintain existing systems, and ensure our AI services meet LifeMD’s standards. This role will operate across our entire AI stack: from service development and deployment to testing frameworks to production monitoring systems.

Responsibilities 

  • Own and maintain the AI infrastructure, including deployment pipelines, testing frameworks, and evaluation systems
  • Build new AI capabilities and use cases while maintaining existing services and systems
  • Develop and maintain AI evaluation models for regression testing
  • Implement monitoring and alerting systems for AI service performance, cost management, and safety compliance
  • Collaborate with engineering teams to integrate AI testing into deployment pipelines and ensure proper governance
  • Manage BigQuery datasets, data pipelines, and cloud infrastructure supporting AI operations
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