Azure AI Platform Architect

We are seeking a highly technical and hands-on Azure AI Platform Architect to lead how our clients set up the enterprise agentic stack on Microsoft Azure. In this role, you will sit at the intersection of Azure platform depth, AI platform architecture, and client advisory, as part of our AI Platform Center of Excellence.

Clients increasingly come to us wanting their entire agentic stack built on Azure: the infrastructure substrate, the agentic services layer (LLM gateway, agent and tool/MCP registries, evals and guardrails, data connectors, runtime observability, Agent/LLM Ops, and FinOps), and the experience layer through which both business users and engineers build agents. Microsoft provides the foundational services; you will bring the architectural judgment that turns them into a platform an enterprise can consume safely, at scale, and repeatably.

This is a senior individual contributor role. You will work as a peer to our AI Architects, with a clear path to leading a small pool of Azure-specialist AI platform engineers as the practice grows.

As part of this role, you will:

• Lead Client Advisory and Pre-Sales: Assess a client's current Azure estate and AI operating model, and shape a clear roadmap for standing up the agentic stack on Azure AI Foundry and Azure OpenAI Service. Be the technical authority in the room who earns the client's confidence that Tiger knows Azure.
• Architect the Agentic Services Layer: Design how Azure AI Foundry capabilities (Azure OpenAI, Agent Service, Model Catalog, Content Safety, Prompt flow, Azure AI Search, and API Management gateways) are composed for enterprise consumption across subscriptions. You will cover multi-tenant management group topologies, Microsoft Entra ID and Workload Identities for agents, shared gateways, default guardrails, network isolation, and cost attribution/FinOps.
• Guide Delivery to the Promise: Advise delivery teams through implementation so what was designed is what gets built. Review architectures, Infrastructure-as-Code (IaC) intent, and security configurations, resolving complex platform challenges as they arise.
• Build Golden Paths and Kits: Codify Azure setup knowledge into reference architectures, Bicep/Terraform recipes, golden paths, and reusable deployment kits that our largely platform-agnostic team can deploy repeatedly.
• Support Internal AI Applications: Advise on Tiger's internal AI applications running on Azure, continually optimizing their architecture, security controls, operations, and cost efficiency.

Who do we expect?

• Professional Experience: Typically 10 to 12 years in technology, with at least 4 to 6 years of hands-on, enterprise-scale work on Microsoft Azure, including 2 to 3 years delivering on Azure OpenAI, Azure AI Studio / Azure AI Foundry, Azure Machine Learning (Azure ML), or Azure's broader ML and data ecosystem.
• Azure Platform Depth: Strong command of how enterprises run Azure: Azure Enterprise Landing Zones, Management Groups and Subscription topology, Microsoft Entra ID (Azure AD), Role-Based Access Control (RBAC), Service Principals, Managed Identities (User/System-assigned), Virtual Networks (VNets), Private Endpoints/Service Endpoints, Azure Policy, API Management (APIM), and Log Analytics / Azure Monitor.
• AI Platform Architecture: Proven experience architecting ML or GenAI platforms on Azure for shared enterprise consumption, rather than single isolated applications. Deep understanding of model serving, gateway routing, evaluation frameworks, runtime monitoring, and security governance as central platform capabilities.
• Agentic Services Exposure: Hands-on exploration of Azure AI Foundry (including Azure AI Agent Service, Agent Frameworks, Prompt flow, and MCP server integrations). You have built, deployed, and formed practical opinions on agents on Azure, even if not yet at massive production scale.
• IaC and Automation Literacy: Solid understanding of how Azure platform environments are declared through Terraform, Bicep, or ARM templates, with the ability to review code for architectural and security intent. (Infrastructure engineers will handle primary implementation).
• Certifications: Microsoft Certified: Azure Solutions Architect Expert (AZ-305), plus Microsoft Certified: Azure AI Engineer Associate (AI-102) or Microsoft Certified: Cybersecurity Architect Expert (SC-100).
• Client Presence & Leadership: Comfortable leading technical sessions with enterprise client architects, CISO teams, and senior technology stakeholders, effectively translating platform technicalities into business trade-offs.
• Consulting Mindset: Adaptable to working on billable client engagements and balancing time across strategy/advisory, delivery governance, and internal asset creation.