Azure DevOps Engineer (Senior)

Tech Stack:

• Microsoft Azure

• Microsoft Foundry (formerly Azure AI Foundry)

• Azure OpenAI Service

• Azure AI Search

• Terraform / Azure Bicep

• Azure DevOps

• CI/CD

• Azure Virtual Networks (VNets), Private Endpoints, Private DNS

• Microsoft Entra ID, RBAC, Managed Identities

• Azure Key Vault

• Azure Storage

• Azure Monitor, Log Analytics, Application Insights

• Infrastructure as Code (IaC)

• Message Queues & Asynchronous Communication

Required Qualifications:

• Good understanding of enterprise integration and distributed processing patterns, including integration with workflow and delegation platforms, message queues, asynchronous communication, high-volume data processing and large-scale log ingestion and analysis.

  Strong hands-on experience with Microsoft Azure, particularly in cloud infrastructure provisioning, configuration and maintenance.

• Practical experience with Microsoft Foundry (formerly Azure AI Foundry), Azure OpenAI Service, Azure AI Search, including provisioning, configuration and integration with Azure OpenAI.

• Strong knowledge of Infrastructure as Code (IaC) using Terraform and/or Azure Bicep.

• Hands-on experience with Azure DevOps CI/CD pipelines and automated infrastructure deployments.

• Good understanding of Azure networking concepts and services, including VNets, Private Endpoints, Private DNS and network security.

• Solid knowledge of Azure identity and security, including Microsoft Entra ID, RBAC, Managed Identities and Azure Key Vault.

• Experience with Azure Storage and related cloud services.

• Familiarity with observability solutions such as Azure Monitor, Log Analytics and Application Insights.

• Understanding of the AI application lifecycle, including automated deployments, monitoring, maintenance and operational support.

• Very good command of English, both spoken and written.

Nice to Have:

• Experience supporting production-grade AI/GenAI applications hosted on Microsoft Azure.

• Experience designing secure and scalable cloud environments for AI workloads.

• Familiarity with DevOps/MLOps practices and automation across the AI Software Development Lifecycle.

• Understanding of enterprise-level cloud security, governance and operational best practices.

• Experience working in large-scale digital transformation or financial services environments.

• Experienced in using AI tools in day-to-day workflow.

Project Description:

The project is delivered for one of the world's leading international insurance and financial services groups, operating across multiple markets and serving millions of customers globally.

The organisation is undergoing a large-scale digital transformation with a strong focus on cloud technologies, automation, artificial intelligence and the modernisation of core business systems.

The initiative focuses on designing, provisioning and maintaining secure, scalable and reliable Microsoft Azure infrastructure supporting AI-powered solutions and intelligent automation.

A key part of the project is establishing the infrastructure, automation and operational capabilities required to support the end-to-end AI Software Development Lifecycle, from environment provisioning and configuration through automated deployment to monitoring and ongoing maintenance of AI-enabled applications. The solution landscape also involves integration with enterprise platforms and high-volume, distributed processing scenarios, making scalability, reliable asynchronous communication and observability important aspects of the environment.

Scope of Services:

• Design, provision and maintain secure and scalable Microsoft Azure infrastructure for AI-powered applications.

• Build and configure environments based on Microsoft Foundry, Azure OpenAI Service and Azure AI Search.

• Provision and configure Azure OpenAI models and integrate AI services with supporting Azure infrastructure.

• Develop and maintain reusable Infrastructure as Code using Terraform and/or Azure Bicep.

• Design and implement automated infrastructure deployment processes using Azure DevOps CI/CD pipelines.

• Configure Azure networking components, including VNets, Private Endpoints, Private DNS and appropriate network security controls.

• Implement identity, access and secrets management using Microsoft Entra ID, RBAC, Managed Identities and Azure Key Vault.

• Configure storage and supporting Azure services required by AI-enabled applications.

• Establish monitoring and observability using Azure Monitor, Log Analytics and Application Insights.

• Build and maintain scalable integration capabilities for enterprise and AI workloads, including asynchronous processing, messaging, high-throughput data flows and centralised processing of application and infrastructure logs.

• Support integrations with enterprise workflow and delegation platforms, ensuring reliable communication and data processing across distributed systems.

• Support automated deployment, monitoring, troubleshooting and operational maintenance of AI applications.

• Collaborate with software engineering, AI and platform teams to improve the reliability, security and automation of the AI development lifecycle.

• Apply cloud engineering, DevOps and security best practices to ensure environments are maintainable, repeatable, scalable and production-ready.