AI平台架构师
AI Platform Architect
Overview
AI 平台架构师是一个亲力亲为、以 AI 为核心构建和运营的专家，负责 MRO 的 AI 工具平台层——包括代理工具、访问控制以及提示库、技能、可重复工作流、MCP 服务器和连接器所依赖的基础架构。该角色将负责部署和配置企业级 AI 平台，如 Claude、MS Copilot 和 GitHub Copilot；支持 LLM API 操作和云资源；对采用率、使用情况、成本和效率进行分析；并交付轻量级自动化工具，使平台平稳运行。该人员每天使用现代 AI 和代理工具，并具备早期采用者的资源能力，能够快速、务实且动手地将其推广给其他人。该角色还与安全、合规、IT 以及各职能部门的用户紧密合作，快速清理工具以确保安全使用。
Responsibilities
- 管理和配置 AI 工具和平台（Claude、MS Copilot、GitHub Copilot、LLM API 操作等）：与 IT 管理/基础设施和 ServiceDesk 合作，进行部署、功能配置、使用监控和优化。
- 负责大规模 AI 上下文的平台层：提示库、技能、可重复工作流、MCP 服务器和连接器所依赖的注册表、访问控制和基础架构。
- 构建并交付轻量级内部工具和自动化服务：部署脚本、自助部署、使用和成本仪表板以及监控。
- 与 IT 和工程团队合作，负责 AI 转型分析和报告：构建和维护仪表板，跟踪内部和产品嵌入式 AI 计划的采用率、使用情况、每工具成本和投资回报率。
- 监控 AI 工具健康状况，跟踪 token 和计算成本，标记异常情况，支持 AI 工作负载的云运维；与工程和 IT 团队合作处理 LLM API 操作和云资源部署。
- 配置和审查 MCP 服务器、连接器和 AI 工具的安全设置，以实现快速、安全的部署；与 InfoSec 合作，对开发人员和非开发人员群体的 AI 工具进行安全审批和监控。
- 维护 AI 领域的治理和安全加速资产：AI 常见问题解答和安全问卷库；响应企业级 AI 安全审查；根据需要为商业部门的 RFP 回复、合规问卷和合同支持提供 AI 特定内容。
- 支持 AI
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Overview
The AI Platform Architect is a hands-on, AI-native builder and operator who owns the platform layer of MRO's AI tooling — the agentic tools, access control, and the infrastructure on which prompt libraries, skills, repeatable workflows, MCP servers, and connectors live. This role will be provisioning and configuring enterprise AI platforms like Claude, MS Copilot, and GitHub Copilot; supporting LLM API operations and cloud resources; instrumenting adoption, usage, cost, and efficiency analytics; and shipping the lightweight automations that make the platform run smoothly. This person uses modern AI and agentic tools daily and brings early-adopter resourcefulness to rolling them out for others — fast, pragmatic, and hands-on. This role also partners closely with security, compliance, IT, and users across all departments to clear tools for safe use quickly.
Responsibilities
- 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).
Qualifications
Required Qualifications:
- 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).
Total CompensationBase 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.
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Pay Range
USD $106,000.00 - USD $143,000.00 /Yr.Originally posted on Himalayas