Software Engineer II

Microsoft Microsoft · Big Tech · Hyderabad, TS, IN · Software Engineering

Software Engineer II role focused on building AI-native support insights and analytics platform using Azure services. Responsibilities include designing and operating data pipelines, building modern data platforms, developing semantic models, and creating AI-native capabilities like data agents and Copilot experiences. The role involves retrieval, orchestration, and evaluation of model quality, ensuring data governance, and writing clean, tested code. Experience with Azure stack, data architectures, and AI/LLM capabilities is preferred.

What you'd actually do

  1. Design, build, and operate scalable data pipelines and services that ingest and unify customer support signals across channels—voice, chat, Copilot, and knowledge base—powering an AI-native support insights and analytics platform.
  2. Build and maintain a modern data platform on the Azure stack (ADLS Gen2, Azure Synapse, and Microsoft Fabric), applying a medallion (Bronze/Silver/Gold) architecture and a unified data model.
  3. Build AI-native capabilities—data agents, Copilot experiences, knowledge agents, and self-serve report builders—including retrieval, orchestration, and evaluation of model quality.
  4. Ensure data quality, lineage, and governance across the golden layer so insights are trustworthy, cited, and policy-compliant.
  5. Write clean, secure, well-tested code (SQL, Python, C#); participate in code reviews; and uphold engineering excellence and coding standards.

Skills

Required

  • SQL
  • Python
  • C#
  • Scala
  • cloud platform data pipelines and services
  • Azure
  • ADLS
  • Azure Synapse
  • Microsoft Fabric
  • production data or software solutions

Nice to have

  • modern data or lakehouse architectures
  • medallion Bronze/Silver/Gold
  • semantic models
  • Power BI (DAX)
  • AI/LLM-powered capabilities
  • data agents
  • copilots
  • retrieval-augmented generation
  • model evaluation
  • Model Context Protocol (MCP)
  • data quality, lineage, and governance
  • CI/CD
  • observability
  • live-site/DevOps practices
  • customer service and support data
  • large-scale, customer-facing data platforms

What the JD emphasized

  • AI-native support insights and analytics platform
  • AI-native capabilities
  • data agents
  • Copilot experiences
  • retrieval, orchestration, and evaluation of model quality
  • data quality, lineage, and governance

Other signals

  • AI-native support insights and analytics platform
  • Build AI-native capabilities—data agents, Copilot experiences, knowledge agents, and self-serve report builders
  • retrieval, orchestration, and evaluation of model quality