Software Engineer Ii, Data Intelligence

Microsoft Microsoft · Big Tech · Redmond, WA +1 · Software Engineering

Software Engineer II on the Data Intelligence team at Microsoft, responsible for designing, developing, and maintaining AI-driven network and security intelligence solutions. This role involves applying machine learning models for anomaly detection and classification, contributing to Generative AI solutions on Azure OpenAI and M365, and building data engineering pipelines using Spark, Synapse/ADF, and Azure Data Explorer. The role requires collaboration with senior engineers and data scientists, troubleshooting live-site issues, and ensuring the performance and reliability of AI/ML solutions.

What you'd actually do

  1. Develop and maintain features for AI-driven network and security intelligence solutions.
  2. Contribute to building and enhancing machine learning models and Generative AI agents with guidance from senior team members.
  3. Build and support data engineering pipelines using technologies such as Spark, Synapse/ADF, Azure Data Explorer (Kusto), and Fabric.
  4. Collaborate with cross-functional teams, including network engineers and data scientists, to integrate AI/ML solutions into Microsoft’s network infrastructure.
  5. Troubleshoot live-site issues and participate in the DRI (Designated Responsible Individual) rotation.

Skills

Required

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

Nice to have

  • 1+ years experience building data solutions, including databases (SQL/No-SQL) and ETL / data pipelines using cloud services
  • Experience with the Azure Big Data stack (Data Lake, Spark, Hadoop) and distributed processing such as Apache Spark, Azure Data Factory (or Synapse), or equivalent.
  • Experience with Azure, REST APIs, and Web Apps
  • Experience building and/or applying machine learning solutions.
  • Familiarity with LLMs, SLMs, skills and agents
  • Familiarity with network engineering concepts.

What the JD emphasized

  • AI-driven network and security intelligence solutions
  • Generative AI solutions
  • machine learning models
  • data engineering pipelines

Other signals

  • Generative AI solutions
  • machine learning models
  • data engineering pipelines