Software Engineer II

Microsoft Microsoft · Big Tech · Bengaluru, KA, IN · Software Engineering

Software Engineer II role on the Azure AI Foundry Customization team, focused on building and scaling the AI platform for Azure and Microsoft's flagship products. The role involves developing pre-training, mid-training, and post-training solutions, working with LoRA models, and handling inference at scale. Responsibilities include creating abstractions, infrastructure, and features for training, testing, validation, scaling, and optimization of ML algorithms, as well as driving customer-inspired innovations and ensuring code quality and security.

What you'd actually do

  1. Independently use appropriate AI agents, skills, and practices across the software development lifecycle.
  2. Work with appropriate internal stakeholders to understand customer/user requirements for a set of features
  3. Create a clear test strategy that ensures solution quality, executes test plans, and builds testable code.
  4. Create extensible and maintainable product feature code for minimal defects.
  5. Review product feature code to ensure it contains the correct test coverage and follows team standards. Implements debugging tools, tests, logs, and telemetry to verify assumptions.

Skills

Required

  • Bachelor's / Master’s Degree in Computer Science or related technical field AND 3+ years technical engineering experience with coding in languages including, but not limited to, C#, Java, or Python OR equivalent experience.
  • Experience writing production code in building internet scale services and distributed systems.
  • Ability to debug, read code and work on a large and increasing codebase.
  • Excellent communication and presentation skills.
  • Experience collaborating with developers (and other stakeholders) and being a team player

Nice to have

  • Experience with day-to-day AI agents and tool use
  • Experience with AI/ML internals a big plus
  • Engineering knowledge of machine learning systems and data pipelines, a plus.

What the JD emphasized

  • AI agents and tool use
  • AI/ML internals a big plus

Other signals

  • develop pre-training, mid-training, and post-training solutions
  • deploying LoRA models and inferencing at scale
  • train, test, validate, scale and optimize for machine learning algorithms