Senior AI Architect, IT

UPMC; United States; Remote; USD 120,000 - 160,000; Full time

Job summary

A leading company is seeking a Senior AI Architect to drive AI strategies aligned with business initiatives. This role involves collaborating with various stakeholders to develop best practices and oversee the full lifecycle of AI solution development, ensuring compliance and security. The position offers a work-from-home opportunity, making it ideal for professionals looking to impact digital transformation in healthcare.

Qualifications

• Expertise in AI solution development, data science, and security.
• Collaboration with data scientists and AI professionals.

Responsibilities

• Develop business case and define architecture.
• Collaborate on data discovery and model selection.
• Support model training, testing, and deployment.

Skills

AI solution development

data science

security

cloud

Healthcare IT

Job description

Purpose:

The Senior AI Architect position requires a high level of expertise in at least two relevant domains such as AI solution development, data science, security, cloud, integrations, Healthcare IT, or similar. As a Senior AI Architect, you will align AI strategies with the business strategies of multiple projects and/or departmental initiatives. You will lead the creation of best practices, policies, procedures, and other AI documentation. Collaboration with data scientists and AI professionals will be essential to support digital transformation by identifying and piloting use cases. You will gather inputs from stakeholders—including business users, data scientists, security professionals, data engineers, analysts, and IT operations—to ensure technical implementations meet current and future requirements.

Work From Home Opportunity!!

Responsibilities:

The AI architect role covers the entire lifecycle of AI solution development. The key responsibilities at each stage are as follows:

• • Develop Business Case: Collaborate with stakeholders to define the architecture, outcomes, and success metrics.
  • Data Discovery: Work with data architects, analytics teams, and data scientists to identify and ensure access to required data, considering privacy, security, and compliance issues.
  • Model Selection: Support the identification of the appropriate foundation model to meet business needs.
  • Model Training and Testing: Ensure availability of environments and resources, develop training and testing plans, and analyze results for improvements.
  • Model Deployment: Collaborate to develop rollout plans for AI solutions.
  • Continuous Monitoring: Support monitoring plan development and participate in governance as needed.