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Microsoft Industry Solutions – Global Center for Innovation and Delivery (GCID) delivers end‑to‑end, industry‑aligned solutions by accelerating customer adoption of Microsoft technologies. With a global team of 1,000+ professionals, GCID offers early‑career engineers a collaborative environment to work on real‑world customer challenges and build consulting, cloud, and application development skills. As a Consultant – Full Stack AI Application Developer (Azure), you will contribute to the design, development, and deployment of modern cloud‑based applications, partnering with senior engineers and architects. This role is ideal for developers eager to strengthen full‑stack skills, deepen Azure expertise, and gain exposure to AI‑enabled application patterns in enterprise delivery.
Job Responsibility:
Design and build cloud‑native, full‑stack applications on Microsoft Azure, contributing across UI, API, services, and data layers with high‑quality, secure, and scalable code
Collaborate with architects and senior consultants to translate business requirements into technical designs and implementation plans
participate in design reviews and solution walkthroughs
Own feature delivery end‑to‑end: estimate effort, implement features, write unit/integration tests, perform code reviews, and support deployment through CI/CD pipelines
Integrate backend services and data platforms, implement RESTful APIs, and ensure performance, reliability, and observability (logging, metrics, traces) for production readiness
Apply security, compliance, and Responsible AI principles across application development and operations, following Microsoft and customer governance standards
Identify technical risks and dependencies early
raise and help mitigate issues, support technical escalations, and contribute to contingency plans for smooth delivery
Reuse and contribute to shared frameworks and IP, leveraging best practices, accelerators, and patterns to improve delivery speed and predictability
Collaborate cross‑functionally (e.g., project leads, delivery managers, account teams) to articulate technical value, support adoption, and ensure customer outcomes
Continuously learn and improve, adopt modern engineering practices (DevSecOps, automation, testing, cloud monitoring) and sharing learnings with the team
Requirements:
4-10 years of professional software development experience
Bachelor's degree in computer science, Engineering, or a related discipline, or equivalent practical experience
Microsoft or cloud certifications are preferred
Hands‑on experience designing and building applications using .NET (C#), .NET Web APIs, Node.js with modern front‑end technologies such as JavaScript or TypeScript, and frameworks like Angular or React
Strong understanding of application development across UI, API, service, integration, and data layers
Experience building and consuming RESTful services and integrating backend systems
Working knowledge of relational databases such as Azure SQL, Azure Cosmos DB, PostgreSQL, Azure SQL managed Instance or Azure Database for MySQL
Solid foundation in software engineering principles, including clean code, debugging, testing, and problem‑solving
Hands‑on experience integrating AI capabilities into applications
Practical knowledge of Azure AI services, including Azure AI Foundry and Azure AI Search, and understanding of RAG (Retrieval‑Augmented Generation) patterns
Experience with RAG pipelines, vector search workflows, or agent‑based patterns using tools such as Semantic Kernel, LangChain, or similar frameworks
Experience with prompt engineering and optimization
Working knowledge of Python for implementing AI workflows, automation, or AI‑assisted application components
Understanding and application of Responsible AI principles
Experience working with Microsoft Azure for application hosting, configuration, and deployment
Familiarity with CI/CD pipelines, source control (Git), and automated build and release processes
Basic experience with application monitoring, logging, and troubleshooting in cloud environments
Understanding secure application development practices, including authentication and authorization
Nice to have:
Exposure to evaluation and tuning techniques for improving AI response quality and reliability
Familiarity with microservices and serverless architectures, including Azure Functions, containers, or Kubernetes
Awareness of deployment strategies such as blue/green or canary releases
Exposure to end‑to‑end observability concepts (metrics, traces)
Experience with open‑source technologies or familiarity with platforms beyond Azure (e.g., Java, AWS, or Google Cloud Platform)
Domain exposure to industries such as financial services, healthcare, manufacturing, or retail