Job Description & Details
This is a heavy-hitting AI Architect gig requiring someone who can actually walk the walk across both Microsoft Azure and Google Cloud ecosystems. You will be knee-deep in modernizing legacy monoliths while simultaneously shipping bleeding-edge Generative AI features using Gemini Enterprise and Azure OpenAI. Be warned that the client is strict on the hybrid arrangement, demanding four days onsite in Houston with zero exceptions.
What You'll Actually Be Doing
Your day-to-day will be a mix of high-level strategy and hands-on technical governance. You'll spend mornings designing microservices architectures and mapping out migration strategies from on-prem to Azure, and afternoons figuring out how to implement Retrieval-Augmented Generation (RAG) pipelines and vector databases for enterprise copilots. You are expected to establish architectural guardrails that engineering teams will actually follow, while balancing cloud costs, security compliance, and performance bottlenecks across complex hybrid environments.
The Core Tech Stack
You need deep, production-level battle scars in Azure PaaS, AKS, Azure Functions, and API Management, paired with solid GCP knowledge specifically around Gemini Enterprise for CX. On the data and AI side, fluency in Azure AI Search, Semantic Kernel, Cosmos DB, and Terraform/Bicep is non-negotiable. The client isn't looking for a PowerPoint architect; they need someone who can drop into an enterprise integration challenge involving Service Bus or Event Hubs and guide developers on proper DevSecOps pipelines using Azure DevOps or GitHub Actions.
Interview Expectations
Expect the hiring panel to test your real-world battle scars by asking how you would handle a scenario where a legacy monolithic database needs to be decoupled into microservices without dropping customer-facing availability. They are secretly looking for your pragmatic approach to failure modes, caching strategies, and data consistency models. You'll also likely be grilled on designing a secure RAG architecture using Azure OpenAI and vector databases, where the interviewer will probe your understanding of latency trade-offs, token limits, and hallucination mitigation in enterprise environments.
Application Advice
Your resume needs to scream 'Enterprise Modernization and AI Delivery' right out of the gate. Make sure to explicitly highlight numbers and scale: size of teams led, volume of data migrated, and specific metrics around cloud cost optimization or GenAI deployment successes. Mirror the exact terminology from the job description—such as Azure PaaS, Gemini Enterprise, Terraform, and RAG architectures—so you effortlessly clear the automated ATS filters before a human technical recruiter reads your profile.