Principal AI Platform Engineer

🕒 January 15

🇺🇸 United States – Remote

💵 $179k - $199k / year

⏰ Full Time

🔴 Lead

🏗️ Platform Engineer

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PointClickCare

WebsiteLinkedInAll Job Openings

1001 - 5000 employees

⚕️ Healthcare Insurance

☁️ SaaS

🏢 Enterprise

💰 Secondary Market on 2022-03

Healthcare Insurance • SaaS • Enterprise

PointClickCare is a cloud-based software provider focused on improving care collaboration and financial health in the healthcare industry. The company offers a comprehensive platform that connects care teams with important data, allowing for seamless care transitions and better patient outcomes. By streamlining operations for skilled nursing facilities, senior living communities, and other healthcare providers, PointClickCare helps reduce inefficiencies, manage medication orders, ensure compliance, and improve financial performance. PointClickCare also fosters innovation and quality improvements in healthcare through integrated care coordination and actionable insights across data silos.

📋 Description

• Design, build, and maintain the core infrastructure layer supporting GenAI products, including model gateways, prompt/versioning stores, vector databases, and LLM evaluation tools. • Implement secure access controls and authentication mechanisms integrated by default into the AI platform components. • Develop and manage observability, monitoring, and logging solutions for GenAI workloads and infrastructure. • Collaborate closely with product and engineering teams to integrate GenAI infrastructure with agent frameworks, and downstream applications. • Optimize infrastructure for scalability, high availability, cost efficiency for production workloads.

🎯 Requirements

• Extensive experience building and maintain AI platform infrastructure, Kubernetes, and container security. • Demonstrated expertise in observability, and monitoring frameworks, with a focus on real-time performance (i.e: experience with OpenTelemetry, MLFlow). • Experience with AI infrastructure components such as vector databases, prompt/versioning stores, and AI IDEs. • Familiarity with vLLM, SGLang or similar framework to host LLM inference workloads. • Experience with CI/CD pipelines and automation for AI model deployment and platform operations • Strong knowledge of authentication and authorization frameworks integrated into AI platforms.