AI Platform Architect

MRO

. Administer and configure AI tools and platforms including Claude, MS Copilot, GitHub Copilot, and LLM APIs .

Posted 9/17/2026full-timeRemote • United StatesMid-LevelSenior💰 $106,000 - $143,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in administering and configuring AI tools and platforms, with a strong focus on cloud resource management and compliance within regulated industries. Proficient in data analysis, automation, and technical documentation to support AI initiatives and governance.

Highest-signal resume keywords

AI Tool AdministrationCloud Resource ProvisioningData Analysis and ReportingTechnical WritingHIPAA Compliance

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

AI Tool ConfigurationCloud Experience (GCP, Azure, AWS)Scripting (Python)Container Management (Docker, Kubernetes)CI/CD and Infrastructure-as-Code (Terraform)

Soft Skills

InitiativeResourcefulness

Tools & Technologies

ClaudeMS CopilotGitHub CopilotMCP ServersAI Platforms (Vertex AI, Azure AI Foundry, Bedrock)

Certifications & Qualifications

SOC 2 Type IIHITRUST

Industry Keywords

HealthcareAI GovernancePHI HandlingCompliance QuestionnairesClinical Data Workflows

Tech Stack

Tools & technologies

AWSAzureBigQueryCloudDockerGoogle Cloud PlatformKubernetesPythonSQLTerraform

About the role

Key responsibilities & impact

Administer and configure AI tools and platforms including Claude, MS Copilot, GitHub Copilot, and LLM APIs
Provision tools, configure features, monitor usage, and optimize platforms in partnership with IT administration, infrastructure, and ServiceDesk
Own the registry, access control, and infrastructure supporting prompt libraries, skills, repeatable workflows, MCP servers, and connectors
Build and ship lightweight internal tooling and automation, including deployment scripts, self-service provisioning, usage and cost dashboards, and monitoring
Build and maintain dashboards tracking adoption, usage, cost-per-tool, and ROI across internal and product-embedded AI initiatives
Monitor AI tool health, token and compute costs, anomalies, and 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
Partner with InfoSec on AI tool security approval and monitoring
Maintain AI FAQs and security questionnaire libraries
Respond to enterprise AI security reviews and contribute AI-specific content to RFP responses, compliance questionnaires, and contracting support
Support AI vendor due diligence and third-party risk assessments
Develop and maintain AI governance documentation, including risk frameworks, AI workflow catalogs, and PHI handling protocols

Requirements

What you’ll need

Daily hands-on use of modern AI and agentic tools
Experience configuring, deploying, or administering AI tools for others
Initiative and resourcefulness in AI, including self-directed learning, side projects, or internal experiments
3–5 years in technical roles spanning cloud, platform, DevOps, or AI operations
Experience ideally within healthcare or another regulated industry
Hands-on cloud experience across GCP, Azure, or AWS
Experience provisioning and configuring cloud resources
Experience working with containers/services such as Docker, Kubernetes, or equivalent
Scripting and automation proficiency in Python or similar
Strong data analysis, reporting, and dashboard-creation skills
Experience administering enterprise SaaS platforms, including user management, SSO configuration, usage analytics, and cost tracking
Working knowledge of HIPAA Privacy/Security Rules, SOC 2 Type II, or HITRUST frameworks and their application to AI tooling
Strong technical writing skills for documentation, governance, and security artifacts
Preferred: hands-on DevOps tooling experience with CI/CD and Infrastructure-as-Code, such as Terraform
Preferred: cloud cost management and FinOps practices
Preferred: AI/ML-specific security considerations, including model governance, prompt-injection risks, MCP/connector security, and data handling
Preferred: enterprise AI platforms such as Vertex AI, Azure AI Foundry, or Bedrock
Preferred: familiarity with BI/analytics pipelines including BigQuery, SQL, and Python
Preferred: background in healthcare data exchange, including FHIR, HL7, and clinical data workflows

Benefits

Comp & perks

Annual cash bonus
Medical insurance
Dental insurance
Vision insurance
Life insurance
401(k) plan
Comprehensive benefits offering