Data and AI Solution Architect

Microsoft Microsoft · Big Tech · Kuala Lumpur, Singapore +1 · Solution Architecture

This role focuses on designing and leading end-to-end enterprise data, GenAI, Copilot, and agentic AI architectures. The Solution Architect will translate business requirements into scalable, secure, and governed solutions, own data platform design for AI readiness, and define GenAI solution patterns including RAG and grounding strategies. They will also lead the architecture for Copilot and AI-augmented applications, integrating AI services into business workflows.

What you'd actually do

  1. You will gather customer/partner insights from a broad range of stakeholders as well as the main business sponsor to shape and form both the definition and ongoing execution of projects and work with customer/partner stakeholders and business sponsor to socialize both the business solution and the project approach to determine if changes are needed to the business solution.
  2. You will use evidence-based approach to represent the customer/partner as a customer advocate and share insights with Product Engineering teams and the business with a view to improving Microsoft technologies, products, services and offerings such that they can better meet customer/partner needs across a territory.
  3. You will define and document the Architecture through an architecture description document, an architecture decisions log, and a requirements/constraints traceability matrix to communicate the value proposition of the business solution along with the project approach.
  4. You will work with customers to understand and demonstrate business value (e.g., release of revenue, cost savings) that the business solution realizes and manage and resolve ambiguity in the requirements and constraints and documents assumptions and implications where it cannot be resolved.
  5. You will generate new and/or improvements to existing intellectual property.
  6. You will identify which ideas should be culled, with consideration for scale across customers and drive the re-use of intellectual property and recommends practices in both pre-sales and delivery as well as participate and contribute to internal/external communities.
  7. You will lead virtual teams around technologies and customer/partner challenges by sharing ideas, insight, and strategic, technical input with technical teams, internal communities across the field and the larger virtual team across Microsoft using knowledge of Microsoft architectures and their context in the competitive landscape.

Skills

Required

  • design and lead end-to-end enterprise data, GenAI, Copilot, and agentic AI architectures
  • translate business outcomes into scalable, secure, and governed solutions across cloud data platforms and AI services
  • Own data platform design (lakehouse/data lake/warehouse, streaming, analytics, semantic models) to ensure the enterprise data estate is AI‑ready (quality, lineage, governance, observability)
  • Lead data migration and modernization from on‑premises environments to cloud
  • Architect and drive cross‑cloud / multi‑cloud data integration and migration scenarios
  • Define GenAI solution patterns, including prompt orchestration, embeddings, vector search, retrieval‑augmented generation (RAG), and grounding strategies that connect LLMs to enterprise data and systems
  • Lead architecture for Copilot and AI‑augmented applications, integrating AI services into business workflows and productivity scenarios while aligning to enterprise architecture and security standards
  • Design and govern agentic AI architectures

Nice to have

  • Microsoft Cloud
  • Microsoft AI services
  • enterprise architecture
  • security standards

What the JD emphasized

  • enterprise data, GenAI, Copilot, and agentic AI architectures
  • GenAI solution patterns
  • Copilot and AI-augmented applications
  • agentic AI architectures

Other signals

  • design and lead end-to-end enterprise data, GenAI, Copilot, and agentic AI architectures
  • Define GenAI solution patterns, including prompt orchestration, embeddings, vector search, retrieval-augmented generation (RAG), and grounding strategies
  • Lead architecture for Copilot and AI-augmented applications
  • Design and govern agentic AI architectures