Software Engineer 2

Overview Microsoft’s AI infrastructure is evolving rapidly to support the next generation of large-scale AI training and inference. The AI Frameworks (AIFx) Networking & Systems Tools (NeST) organisation develops foundational system software that enables Microsoft’s Maia accelerator platforms across pre-silicon development, hardware bring-up, cloud integration and production cloud infrastructure that enables AI accelerators to operate reliably and efficiently at cloud scale.

Within NeST, the India Development Centre is building end-to-end engineering competency across the Maia system software stack. Our work spans the boundary between distributed cloud systems and low-level accelerator software, including control-plane services, host and device management software, accelerator virtualisation, Kubernetes-based infrastructure, Developer/Debugger Infrastructure tools, hardware lifecycle management, reliability, telemetry and diagnostics.

We are looking for a Software Engineer 2 to design and build scalable AKS/Kubernetes infrastructure for GPU and AI accelerator platforms. You will work on cluster lifecycle, node and resource management, deployment, availability, observability and automation needed to operate accelerator infrastructure reliably at cloud scale.

Microsoft’s mission is to empower every person and every organization on the planet to achieve more. As employees we come together with a growth mindset, innovate to empower others and collaborate to realize our shared goals. Each day we build on our values of respect, integrity, and accountability to create a culture of inclusion where everyone can thrive at work and beyond.

Responsibilities

• Design and develop scalable AKS/Kubernetes infrastructure for GPU and AI accelerator environments.
• Build services and automation for cluster provisioning, configuration, upgrades and lifecycle management.
• Develop solutions for node lifecycle, health monitoring, failure detection and automated recovery.
• Improve infrastructure scalability, reliability and availability across large accelerator fleets.
• Build Kubernetes integrations for accelerator discovery, resource management, scheduling and workload enablement.
• Develop reliable software and automation for deployment, configuration and management of accelerator infrastructure.
• Improve telemetry, observability, diagnostics and operational readiness of distributed infrastructure.
• Diagnose complex issues across Kubernetes, containers, Linux, networking and accelerator infrastructure.
• Collaborate with Control Plane, systems software, hardware and platform teams to deliver end-to-end solutions.
• Contribute to architecture/design reviews, engineering practices, code quality and mentoring of other engineers.
• Apply AI-assisted engineering practices across design, coding, testing, debugging, code reviews and documentation to improve engineering velocity and quality.
• Use AI-assisted workflows to accelerate code comprehension, troubleshooting, root-cause analysis, test development and infrastructure automation.
• Leverage AI to accelerate learning and build deeper AKS/Kubernetes, distributed systems and AI infrastructure domain competency.
• Identify repetitive engineering and operational workflows that can be simplified or automated using AI-enabled engineering approaches.
• Share reusable AI-assisted engineering practices and technical knowledge to improve team productivity and engineering competency.

Qualifications Required Qualifications:

• Bachelor’s Degree in Computer Science, Computer Engineering or related technical discipline, or equivalent experience with 4+ years of industry relevant experience.
• Software development skills in Go, C++, C#, Python or similar languages.
• Experience building distributed systems, cloud infrastructure or platform services.
• Hands-on experience with Kubernetes, containerisation and cloud-native technologies.
• Understanding of distributed-systems concepts including scalability, concurrency, state management, resiliency and failure recovery.
• Experience developing reliable production software and debugging complex distributed systems.
• Design, problem-solving and cross-team collaboration skills.
• Demonstrated ability or experience using AI-assisted software engineering tools and workflows to improve development effectiveness, debugging, automation and software quality.
• Ability to rapidly develop expertise in complex infrastru…