Cloud Architect

HAN Staffing; United States; On-site; USD 180,000 - 240,000; Full time

Job summary

HAN Staffing is seeking an experienced Azure AI Gateway & Platform Architect to establish and operationalize an enterprise-scale AI platform on Azure. The role focuses on governance, Azure AI Gateway architecture, secure AI service consumption, observability, and reliability engineering for GenAI workloads.

The candidate will drive scalable AI consumption models, onboard business units securely, and enable GenAI deployment with strong governance and risk management across the organization.

Qualifications

• Experience designing enterprise-grade AI platforms on Azure.
• Knowledge of Azure OpenAI, API Management, and governance controls.
• Experience implementing AIOps, observability, and reliability engineering.

Responsibilities

• Design and establish an enterprise-grade Azure AI Platform.
• Define architecture standards, landing zones, governance controls, and reference architectures for AI workloads.
• Create reusable platform patterns for GenAI, RAG, Agentic AI, and AI-assisted automation solutions.
• Enable secure onboarding of business units and development teams onto the AI platform.
• Define enterprise AI operating model, platform lifecycle, and service management framework.
• Design and implement Azure AI Gateway strategy leveraging Azure API Management.
• Establish centralized routing, throttling, cost management, security, monitoring, and policy enforcement for AI services.
• Build abstraction layers for Azure OpenAI, third-party LLMs, embedding models, vector databases, and AI services.
• Implement AI service catalog and model management framework.
• Enable multi-model orchestration and model governance.
• Implement responsible AI controls, security guardrails, auditability, and compliance requirements.
• Establish identity management, RBAC, secrets management, and access controls.
• Drive implementation of data protection, AI risk management, and regulatory compliance practices.
• Partner with security teams to review AI workloads and platform architecture.
• Establish enterprise AIOps framework using Azure Monitor, Log Analytics, Application Insights, and Open Telemetry.
• Implement AI-driven anomaly detection, predictive analytics, root cause analysis, and automated remediation.
• Design self-healing operational workflows and intelligent incident management processes.
• Build operational dashboards, observability platforms, and reliability metrics.
• Reduce MTTR, improve service availability, and automate operational response activities.
• Develop Infrastructure-as-Code solutions using Terraform/Bicep.
• Automate deployment, configuration, governance, and compliance validation.
• Enable CI/CD integration for AI services and platform components.
• Implement platform monitoring, health checks, capacity planning, and performance optimization.
• Work closely with Enterprise Architecture, Cloud Engineering, Security, Data, and AI Engineering teams.
• Establish architecture review processes and platform governance councils.
• Present roadmap, architecture, and operational metrics to leadership and customer stakeholders.
• Mentor engineering teams on AI platform best practices.

Skills

Azure Identity & Access Management

Agentic AI Architecture

AI Gateway Patterns

Prompt Engineering

Vector Databases

LLM Deployment & Operations

REST APIs

Microservices

Service Mesh

Application Insights

Open Telemetry

Self-Healing Automation

Terraform

Bicep

Policy as Code

Security Automation

Infrastructure as Code

Tools

Terraform

Bicep

OpenTelemetry

Azure Monitor

Job description

Location: Remote

Experience:

Role Summary:

We are seeking an experienced Azure AI Gateway & Platform Architect (AIOps Lead) to establish and operationalize an enterprise-scale AI platform on Azure. The role will be responsible for designing and implementing AI governance frameworks, Azure AI Gateway architecture, secure AI service consumption, observability, reliability engineering, and AI-driven operations (AIOps).

The ideal candidate will have deep expertise in Azure Cloud, Azure OpenAI, API Management, AI platform engineering, monitoring, automation, and large-scale enterprise platform deployment. The individual will drive the creation of a secure, scalable, and governed AI consumption model while enabling business teams to build and deploy GenAI solutions efficiently.

Key Responsibilities:

• Design and establish an enterprise-grade Azure AI Platform.
• Define architecture standards, landing zones, governance controls, and reference architectures for AI workloads.
• Create reusable platform patterns for GenAI, RAG, Agentic AI, and AI-assisted automation solutions.
• Enable secure onboarding of business units and development teams onto the AI platform.
• Define enterprise AI operating model, platform lifecycle, and service management framework.
• Design and implement Azure AI Gateway strategy leveraging Azure API Management.
• Establish centralized routing, throttling, cost management, security, monitoring, and policy enforcement for AI services.
• Build abstraction layers for Azure OpenAI, third-party LLMs, embedding models, vector databases, and AI services.
• Implement AI service catalog and model management framework.
• Enable multi-model orchestration and model governance.

AI Governance & Security

• Implement responsible AI controls, security guardrails, auditability, and compliance requirements.
• Establish identity management, RBAC, secrets management, and access controls.
• Drive implementation of data protection, AI risk management, and regulatory compliance practices.
• Partner with security teams to review AI workloads and platform architecture.
• Establish enterprise AIOps framework using Azure Monitor, Log Analytics, Application Insights, and Open Telemetry.
• Implement AI-driven anomaly detection, predictive analytics, root cause analysis, and automated remediation.
• Design self-healing operational workflows and intelligent incident management processes.
• Build operational dashboards, observability platforms, and reliability metrics.
• Reduce MTTR, improve service availability, and automate operational response activities.
• Develop Infrastructure-as-Code solutions using Terraform/Bicep.
• Automate deployment, configuration, governance, and compliance validation.
• Enable CI/CD integration for AI services and platform components.
• Implement platform monitoring, health checks, capacity planning, and performance optimization.

Stakeholder Management

• Work closely with Enterprise Architecture, Cloud Engineering, Security, Data, and AI Engineering teams.
• Establish architecture review processes and platform governance councils.
• Present roadmap, architecture, and operational metrics to leadership and customer stakeholders.
• Mentor engineering teams on AI platform best practices.

Required Skills:

• Azure Identity & Access Management

AI & GenAI

• Agentic AI Architecture
• AI Gateway Patterns
• Prompt Engineering
• Vector Databases
• LLM Deployment & Operations

API & Integration

• REST APIs
• Microservices
• Service Mesh

AIOps & Observability

• Application Insights
• Open Telemetry
• Self-Healing Automation

DevSecOps

• Terraform / Bicep
• Policy as Code
• Security Automation
• Infrastructure as Code

Preferred Qualifications:

• Microsoft Certified: Azure Solutions Architect Expert
• Experience implementing Azure OpenAI platforms for Banking or Financial Services clients.
• Experience establishing enterprise AI Centers of Excellence (CoE).
• Experience with GitLab Duo, GitHub Copilot, MCP, Agentic AI, and Enterprise AI Governance.