We are seeking an experienced Azure Systems Architect to lead the design, governance, and implementation of scalable, secure, cost-efficient, and AI-ready cloud solutions on Microsoft Azure. In this role, you will define Azure hosting patterns, networking, security, governance, platform integration standards, container platform architecture, DevOps practices, and AI solution patterns. You will ensure that solutions align with enterprise architecture principles, cloud best practices, security requirements, and modern engineering delivery models. You will work closely with Platform Engineering, DevOps, Security, Networking, Data, AI, and Product teams to establish a robust Azure foundation that enables scalable application delivery, operational excellence, and the adoption of AI-enabled engineering and business capabilities. Responsibilities Define and maintain the enterprise Azure architecture, including landing zones, subscription strategy, networking, identity, governance, security, platform services, and workload hosting standards Design and standardize reusable cloud patterns for application hosting, APIs, front-end applications, microservices, containerized workloads, serverless workloads, data integrations, and supporting platform services Define architecture patterns for Azure Kubernetes Service, containerized workloads, ingress, service mesh, workload identity, secrets management, container security, scaling, observability, and operational readiness Establish secure and scalable networking architectures, including hub-and-spoke connectivity, private networking, DNS, firewalls, private endpoints, API gateways, and workload integration patterns Define and oversee implementation standards for Infrastructure as Code, preferably Terraform, including reusable modules, environment promotion, policy-as-code, and automated compliance controls Drive DevOps and CI/CD architecture standards, including branching strategies, build and release pipelines, environment separation, quality gates, security scanning, automated testing, and deployment automation Promote modern engineering practices using GitHub, GitHub Actions, Azure DevOps, GitHub Copilot, and AI-assisted software delivery, including AI-supported CI/CD, code review, documentation, testing, and platform automation Produce robust, scalable, and secure cloud-native solutions leveraging Azure AI, Azure OpenAI, Azure AI Search, Azure Machine Learning, and related Azure AI services Design AI-enabled solutions based on modern patterns such as RAG, agentic workflows, multi-agent architectures, AI orchestration, tool/function calling, prompt management, evaluation, grounding, guardrails, and responsible AI controls Define and oversee AI-enabled solutions for document processing, classification, validation, content generation, knowledge discovery, workflow automation, and enterprise search Drive integration of AI capabilities into existing enterprise platforms and applications to enhance communication, data processing, automation, productivity, and operational efficiency Establish observability, security, compliance, resiliency, and cost-management practices using Azure-native capabilities and enterprise governance frameworks Provide architectural guidance, review solution designs, challenge implementation approaches, and ensure alignment with enterprise standards and target-state architecture Collaborate with product, platform, security, data, and engineering teams to translate business objectives into practical cloud, DevOps, container, and AI architecture decisions Requirements Proven experience designing and implementing enterprise-scale Azure cloud architectures Strong expertise in Azure networking, identity management, governance, security, landing zones, platform services, and workload hosting models Deep understanding of Azure Landing Zones, hub-and-spoke network architectures, private connectivity, DNS, firewalls, private endpoints, and workload integration patterns Hands-on experience with Azure Kubernetes Service, containers, microservices, container registries, ingress controllers, workload identity, autoscaling, and container platform operations Experience designing serverless architectures using services such as Azure Functions, Logic Apps, Event Grid, Service Bus, API Management, and related integration services Hands-on experience with Infrastructure as Code, preferably Terraform, including reusable modules, multi-environment deployments, and integration with CI/CD pipelines Strong experience designing cloud deployment models and working closely with DevOps teams on CI/CD, automated testing, security scanning, deployment automation, release governance, and operational handover Practical understanding of GitHub, GitHub Actions, Azure DevOps, GitHub Copilot, and AI-assisted engineering workflows 2+ years of experience architecting, deploying, or governing solutions with AI/Generative AI technologies, specifically Azure AI, Azure OpenAI, Azure AI Search, or agent-based workloads Experience designing AI solution architectures using patterns such as RAG, agentic workflows, multi-agent systems, prompt orchestration, AI evaluation, grounding, guardrails, and responsible AI Experience implementing observability solutions using Azure Monitor, Application Insights, Log Analytics, Container Insights, distributed tracing, dashboards, and alerting Knowledge of Azure cost management, FinOps, capacity planning, and cost optimization practices Strong understanding of cloud security, DevSecOps, secrets management, managed identities, RBAC, policy enforcement, and compliance-by-design Strong stakeholder management, communication, documentation, and cross-functional collaboration skills Ability to operate at both strategic and hands-on architecture levels, from enterprise standards to practical implementation guidance Nice to have Microsoft Azure certifications, especially Azure Solutions Architect Expert Kubernetes certifications or strong practical experience with production-grade Kubernetes platforms Experience working in regulated industries with strong compliance, auditability, governance, and data protection requirements Familiarity with Azure API Management, event-driven architectures, microservices, and integration platforms Experience with Azure AI Foundry, Semantic Kernel, LangChain, LangGraph, AutoGen, or similar AI orchestration frameworks Experience designing AI platforms with agent registries, tool catalogs, model gateways, prompt/version management, evaluation pipelines, and AI observability Experience with platform engineering, internal developer platforms, golden paths, reusable templates, and self-service cloud capabilities Familiarity with SRE practices, reliability engineering, chaos testing, performance testing, and production readiness reviews
