AI Platform Architect

• Administer and configure AI tools and platforms (Claude, MS Copilot, GitHub Copilot, LLM API operations, and others): provisioning, feature configuration, usage monitoring, and optimization, in partnership with IT admin/infrastructure and ServiceDesk.
• Own the platform layer for AI context at scale: the registry, access control, and infrastructure on which prompt libraries, skills, repeatable workflows, MCP servers, and connectors live.
• Build and ship lightweight internal tooling and automation in service of the platform: deployment scripts, self-service provisioning, usage and cost dashboards, and monitoring.
• Own AI transformation analytics and reporting in partnership with IT and Engineering: build and maintain dashboards tracking adoption, usage, cost-per-tool, and ROI across internal and product-embedded AI initiatives.
• Monitor AI tool health, track token and compute costs, flag anomalies, and support cloud operations for AI workloads; partner with Engineering and IT on LLM API operations and cloud resource deployment.
• Configure and review security settings for MCP servers, connectors, and AI tools to enable fast, safe rollouts; partner with InfoSec on AI tool security approval and monitoring across developer and non-developer populations.
• Maintain AI governance and security-acceleration assets for the AI domain: AI FAQs and the security questionnaire library; respond to enterprise AI security reviews; contribute AI-specific content to RFP responses, compliance questionnaires, and contracting support for the commercial org as needed.
• Support AI vendor due diligence and third-party risk assessments; develop and maintain AI governance documentation (risk framework, AI workflow catalog, and PHI handling protocols).

• Demonstrated AI-native fluency: daily hands-on use of modern AI and agentic tools and experience configuring, deploying, or administering these tools for others - not just using them.
• Demonstrated initiative and resourcefulness in AI (self-directed learning, side projects, internal experiments); often the recognized go-to person for AI tooling questions on their current team.
• 3-5 years in technical roles spanning cloud, platform, DevOps, or AI operations, ideally within healthcare or another regulated industry.
• Hands-on cloud experience across GCP, Azure, or AWS: provisioning and configuring resources, and working with containers/services (e.g., Docker, Kubernetes, or equivalent).
• Scripting and automation proficiency (Python or similar), plus strong data analysis, reporting, and dashboard-creation skills.
• Experience administering enterprise SaaS platforms (user management, SSO configuration, usage analytics, cost tracking).
• Working knowledge of HIPAA Privacy/Security Rules, SOC 2 Type II, or HITRUST frameworks, and how they apply to AI tooling.
• Strong technical writing skills for documentation, governance, and security artifacts.

Preferred Qualifications:

• Hands-on DevOps tooling experience: CI/CD and Infrastructure-as-Code (e.g., Terraform).
• Experience with cloud cost management and FinOps practices.
• Experience with AI/ML-specific security considerations (model governance, prompt-injection risks, MCP/connector security, data handling).
• Experience with enterprise AI platforms (Vertex AI, Azure AI Foundry, Bedrock).
• Familiarity with BI/analytics pipelines (BigQuery, SQL, Python).
• Background in healthcare data exchange (FHIR, HL7, clinical data workflows).

Base pay is one element of the total compensation package. Eligible employees may also receive an annual cash bonus and have access to a comprehensive benefits offering, including medical, dental, vision, life insurance, and a 401(k) plan. Salary Range It is not typical for an individual to be hired at or near the top of the range. Individual pay may be influenced by factors such as skills, qualifications, experience, licensure, certifications, geographic location, and internal equity. Applicant Privacy Notice