Architect - AI Business Solutions

Microsoft Microsoft · Big Tech · Hyderabad, TS, IN +2 · Solution Architecture

This role focuses on architecting and designing AI solutions, specifically leveraging Microsoft's Copilot and agent technologies, to address customer business challenges. The architect will assess customer AI readiness, translate business requirements into technical solutions using Power Platform, Azure, and Microsoft 365 Copilot, and ensure scalable, secure, and compliant deployments. The role involves close collaboration with customers, product engineering, and delivery teams to drive adoption and validate business value.

What you'd actually do

  1. Engage business sponsors, product owners, Enterprise Architects, Security, and Operations leaders to frame outcomes and define the target agentic scenarios.
  2. Assess customer AI readiness across identity, data, security/compliance, and operating model; identify gaps to unlock Copilot and agent adoption at scale.
  3. Translate business requirements into end-to-end solution options across Power Platform, Azure, and Microsoft 365 Copilot/Copilot Studio extensibility.
  4. Apply structured delivery methodologies (Agile/Iterative/Hybrid) and reference architectures to design scalable Copilot and agentic solutions.
  5. Own and maintain architectural documentation, including agent design specs, integration contracts, and operational runbooks.

Skills

Required

  • Experience with Microsoft Cloud and AI technologies (Azure, Microsoft 365 Copilot, Copilot Studio, Power Platform)
  • Understanding of AI readiness assessment and gap identification
  • Ability to translate business requirements into technical solutions
  • Experience in designing scalable and secure AI/agent solutions
  • Knowledge of architectural documentation and design specs
  • Familiarity with Agile/Iterative delivery methodologies
  • Understanding of compliance and responsible AI principles
  • Experience with GitHub Copilot for development acceleration

Nice to have

  • Experience with Low-Code modernization
  • Familiarity with data grounding and quality attributes for AI
  • Experience in cost-based discussions for AI deployments
  • Knowledge of AI risk posture and responsible AI guidance

What the JD emphasized

  • agentic scenarios
  • agent adoption at scale
  • agentic engagements
  • agentic AI adoption
  • agentic solutions
  • agent capabilities
  • agentic workloads
  • agent extensibility
  • build and manage agents
  • agentic delivery practices

Other signals

  • customer AI readiness
  • Copilot and agent adoption at scale
  • translate business requirements into end-to-end solution options
  • design scalable Copilot and agentic solutions
  • enterprise-grade architecture to support scaled Copilot and agent deployments