Principal Software Engineer - Coreai

Microsoft Microsoft · Big Tech · Mountain View, CA +2 · Software Engineering

Principal Software Engineer to build and operate end-to-end multimodal generative AI systems for Azure Content Understanding, focusing on extracting insights from diverse data types. The role involves technical leadership, system design, quality improvement through evaluation, and collaboration with security and product teams.

What you'd actually do

  1. Design, build, and operate production-grade generative AI and multimodal systems, with end-to-end ownership from concept through deployment and service operations.
  2. Lead technical design for core GenAI capabilities (e.g., retrieval-augmented generation, context and memory, orchestration) and make data-driven tradeoffs across quality, latency, cost, and safety.
  3. Define and improve model and system quality using evaluation frameworks, experiment design, and production telemetry; ensure robust testing and regression coverage.
  4. Collaborate with security, privacy, and compliance partners to build solutions that meet enterprise requirements and align with Responsible AI standards and practices.
  5. Provide technical leadership across teams by setting direction, reviewing designs, unblocking execution, and mentoring engineers on architecture, coding standards, and ML engineering best practices.

Skills

Required

  • Bachelor's Degree in Computer Science or related technical field AND 6+ years technical engineering experience
  • coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python

Nice to have

  • Master's Degree in Computer Science or related technical field AND 8+ years technical engineering experience
  • Bachelor's Degree in Computer Science or related technical field AND 12+ years technical engineering experience
  • Advanced degree in Computer Science, Machine Learning, or related field.
  • Demonstrated technical leadership through influence
  • Experience with prompt engineering, retrieval-augmented generation (RAG), and memory/agent frameworks.
  • Experience building and shipping generative AI systems (including multimodal scenarios).
  • Familiarity with compliance and security standards in enterprise AI solutions.
  • Track record of delivering enterprise-facing AI products at scale.
  • Experience building and operating ML/AI systems in cloud environments; familiarity with MLOps practices (Azure a plus).
  • Experience partnering with cross-functional stakeholders to define requirements and drive technical decisions.

What the JD emphasized

  • end-to-end ownership
  • production-grade
  • enterprise requirements
  • Responsible AI standards

Other signals

  • multimodal AI
  • generative AI
  • end-to-end ownership
  • production systems
  • enterprise requirements