Software Engineering Ii- Full Stack

Microsoft Microsoft · Big Tech · Vancouver, BC +1 · Software Engineering

Full Stack Engineer to build LLM-powered data engineering experiences and infrastructure for Microsoft Fabric. The role involves implementing agentic workflows and scalable LLM-backed data features, focusing on AI Engineering and modern LLM-based systems.

What you'd actually do

  1. Build and ship end-user features in the Fabric
  2. Implement modern React-based UX extension experiences aligned with UX design guidelines and shared UI patterns.
  3. Build and use Fluent UI component libraries and following organization level ensure consistent look and feel across Fabric experiences.
  4. Contribute to backend service code that power Fabric Data Engineering and Data Science experiences, primarily in .NET (C#), Python and related technologies.
  5. Contribute to quality: write/maintain automated tests and participate in E2E testing (e.g., Playwright-based tests) and debugging of test and pipeline issues.

Skills

Required

  • Bachelor's Degree in Computer Science, or related technical discipline AND 2+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python OR equivalent experience
  • 1+ years of experience building production web experiences with modern component-based UI frameworks, especially React
  • 1+ years of experience in engineering fundamentals: code quality, debugging, performance, maintainability, and testing mindset.

Nice to have

  • Understanding of modern LLM systems and AI Engineering: prompting, grounding/RAG, tool/function calling, agent orchestration etc.
  • Experience operationalizing AI/ML features: monitoring, telemetry, experimentation (A/B), rollout strategies, and cost/latency optimization
  • Familiarity with cloud-native engineering on Azure (compute, storage, networking) and secure, compliant data handling
  • Experience collaborating across disciplines (PM, design, research, partner teams) to deliver customer-facing AI capabilities

What the JD emphasized

  • modern LLM systems and AI Engineering
  • agent orchestration
  • grounding/RAG
  • tool/function calling
  • operationalizing AI/ML features
  • cost/latency optimization

Other signals

  • LLM-powered data engineering experiences
  • agentic workflows
  • scalable LLM-backed data features
  • AI Functions integration
  • notebook copilots
  • evaluation/telemetry