Software Engineer

Azure Solution Engineer

Overview

The Azure Solution Engineer is responsible for designing, building, testing, deploying, and supporting end-to-end solutions on the Microsoft Azure platform. This role goes beyond traditional .NET full‑stack development and focuses on building AI-first, cloud-enabled, and automation-driven solutions using a mix of Pro-code (C#, Python, JavaScript/TypeScript and Azure scripting), DevOps, and Azure AI capabilities across the Azure ecosystem.

The engineer will work closely with the Technical Architect and cross-functional teams to deliver scalable, secure, and maintainable Azure solutions. The role requires a strong foundation in Azure solution engineering and the ability to leverage AI, Generative AI, Copilot, and Azure AI Foundry as development accelerators.

Key Responsibilities

Core Azure Solution Development

• Design and develop Azure-based solutions using C#, .NET Core, ASP.NET Core, EF Core, REST APIs, and modern front-end frameworks (Angular / React) where applicable.
• Develop supporting services, tools, and components using Python where it is a natural fit in the Azure ecosystem (e.g., AI integration, automation, background processing).
• Build cloud-native applications using Azure App Service, Azure Functions, Azure Container Apps, and/or AKS, based on solution needs.
• Implement data solutions using Azure SQL, Cosmos DB, Blob Storage, and Table Storage, with attention to scalability and performance.
• Integrate solutions using Azure Service Bus, Event Grid, and Event Hubs for asynchronous and event-driven architectures.
• Apply Azure Well-Architected Framework principles covering reliability, security, performance, cost, and operational excellence.

Pro-Code Engineering & Azure Integrations

• Design and implement custom Azure services, APIs, microservices, and background workers using pro-code approaches.
• Write automation and integration scripts using Python, PowerShell, and Azure CLI for environment setup, deployments, and operational tasks.
• Develop and maintain Infrastructure as Code using Bicep and/or Terraform.
• Integrate enterprise systems using REST APIs, external SaaS APIs, SDKs, and Azure-native integration services.
• Apply appropriate architectural patterns such as microservices, event-driven, serverless, and container-based architectures, based on solution requirements.
• Ensure code quality, maintainability, and extensibility through engineering best practices.

DevOps & Engineering Excellence

• Implement and maintain CI/CD pipelines using Azure DevOps or GitHub Actions.
• Apply containerization using Docker and manage deployments across environments.
• Use scripting and configuration-as-code approaches to standardize environments and reduce manual effort.
• Implement monitoring, logging, and observability using Application Insights, Log Analytics, OpenTelemetry, and structured logging.
• Write unit tests, integration tests, and component tests to ensure solution quality and reliability.

AI Solution Development

• Build AI-enabled solutions using Azure AI Services, Azure OpenAI, and Azure AI Foundry capabilities.
• Implement GenAI use cases such as Copilot, chat-based assistants, document processing, summarization, and knowledge search.
• Use Python and .NET SDKs to integrate AI models and services into applications.
• Understand and apply prompt engineering, embeddings, vector search, and RAG patterns at a foundational level.
• Contribute to Agentic AI solutions, orchestrating workflows using tools, APIs, and AI models.
• Apply Responsible AI, security, and data privacy principles in AI solutions.

AI‑Assisted Development

• Use GitHub Copilot, Azure Copilot, and AI-assisted tools to improve developer productivity and code quality.
• Leverage AI for code generation, refactoring, test creation, documentation, scripting, and troubleshooting.
• Continuously evaluate emerging AI capabilities and adopt them pragmatically within delivery teams.

Required Skills & Experience

• 4 years of experience in software engine

DevOps & Engineering Excellence

• Implement and maintain CI/CD pipelines using Azure DevOps or GitHub Actions.
• Apply containerization using Docker and manage deployments across environments.
• Use scripting and configuration-as-code approaches to standardize environments and reduce manual effort.
• Implement monitoring, logging, and observability using Application Insights, Log Analytics, OpenTelemetry, and structured logging.
• Write unit tests, integration tests, and component tests to ensure solution quality and reliability.

AI Solution Development

• Build AI-enabled solutions using Azure AI Services, Azure OpenAI, and Azure AI Foundry capabilities.
• Implement GenAI use cases such as Copilot, chat-based assistants, document processing, summarization, and knowledge search.
• Use Python and .NET SDKs to integrate AI models and services into applications.
• Understand and apply prompt engineering, embeddings, vector search, and RAG patterns at a foundational level.
• Contribute to Agentic AI solutions, orchestrating workflows using tools, APIs, and AI models.
• Apply Responsible AI, security, and data privacy principles in AI solutions.

AI‑Assisted Development

• Use GitHub Copilot, Azure Copilot, and AI-assisted tools to improve developer productivity and code quality.
• Leverage AI for code generation, refactoring, test creation, documentation, scripting, and troubleshooting.
• Continuously