Principal Architect - Data AI

Microsoft Microsoft · Big Tech · Hyderabad, TS, IN +1 · Solution Architecture

This role focuses on architecting, designing, and delivering AI-driven transformation solutions for enterprise customers, with a strong emphasis on Azure AI services, machine learning models (including generative AI and LLMs), and AI agentic frameworks. The architect will lead client engagements, ensure solution performance and scalability, and guide customers through the deployment and operationalization of AI solutions.

What you'd actually do

  1. Lead client engagements from definition through delivery to ensure technical and AI transformation success for our customers.
  2. Collaborate with enterprise customers to scope and define requirements, architect and design solutions, and deliver transformational, customized industry solutions and services, including AI-powered applications.
  3. Ensure all solutions exhibit high levels of performance, security, scalability, maintainability, repeatability, appropriate reusability, and reliability upon deployment.
  4. Be a Voice of Customer to share insights and best practices, connect with Engineering teams to remove key blockers and drive product improvements.
  5. Proactively identify opportunities for integrating modern architecture patterns and emerging technical solutions—including AI, machine learning, and generative AI—into current designs.

Skills

Required

  • Extensive experience in implementing, operating, customizing, tuning, and troubleshooting Cloud solutions.
  • Designing scalable solutions on cloud platforms with focus on performance and resiliency, utilizing microservices architectures.
  • Engineering distributed applications within architectural scenarios such as Web, IoT, and AI-powered systems.
  • Application design patterns, and anti-patterns, such as MVC, CQRS, SAGA.
  • Interacting with and querying databases and/or NoSQL datastores.
  • Application monitoring and end-to-end telemetry.
  • Containers for packaging application deployment units and interacting with container-orchestration technologies such as Kubernetes, Service Fabric, Cloud Foundry.
  • Defining CI/CD pipelines to automate test and release across different application environments using concepts such as Blue/Green and Canary deployments and related technologies.
  • Source code management using Git or other source control technologies.
  • Open source technologies and frameworks.
  • Experience architecting, designing, and deploying AI solutions using Azure AI or other similar cloud services.
  • Proficiency in building, training, and operationalizing machine learning models, including generative AI and LLMs.
  • Familiarity with AI solution design patterns, responsible AI principles, and ethical AI practices.
  • Hands-on experience with AI agentic frameworks, multi-agent orchestration, and integration of AI agents into enterprise applications.
  • Programming skills in Python, R, and/or C# for AI development.
  • Knowledge of AI certifications and demonstrated learning in the merging areas of AI Stack
  • Design and Implementation approach towards Data governance, logging, and lineage for AI solutions.
  • Experience with cloud databases such as Azure SQL, Azure Database for PostgreSQL, MySQL, MariaDB, and Azure Cosmos DB.
  • Data pipeline creation, ETL/ELT processes, and managing large datasets (structured and unstructured).

What the JD emphasized

  • AI-driven transformation
  • AI-powered applications
  • AI solutions
  • AI, machine learning, and generative AI
  • AI solution prototypes
  • AI solutions
  • AI & Data Science
  • AI solutions
  • generative AI and LLMs
  • AI solution design patterns
  • responsible AI principles
  • ethical AI practices
  • AI agentic frameworks
  • multi-agent orchestration
  • integration of AI agents into enterprise applications
  • AI development
  • AI certifications
  • AI Stack
  • AI solutions
  • AI solutions

Other signals

  • architecting and designing AI solutions
  • building, training, and operationalizing machine learning models
  • AI agentic frameworks, multi-agent orchestration
  • integration of AI agents into enterprise applications