The Role
Design and develop scalable AKS/Kubernetes infrastructure for GPU and AI accelerator platforms. Build cluster lifecycle automation, node management, resource scheduling, deployment systems, health monitoring, failure recovery, observability, diagnostics, and reliability improvements. Troubleshoot distributed infrastructure across Kubernetes, containers, Linux, networking, and accelerator systems. Collaborate across software, hardware, control-plane, and platform teams while contributing to architecture, code quality, mentoring, and AI-assisted engineering practices.
Summary Generated by Built In
Overview
Responsibilities
Qualifications
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 Senior Software Engineer 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 best 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 8+ years of industry relevant experience.
• Strong 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.
• Strong understanding of distributed-systems concepts including scalability, concurrency, state management, resiliency and failure recovery.
• Experience developing reliable production software and debugging complex distributed systems.
• Strong design, problem-solving and cross-team collaboration skills.
• Demonstrated ability or strong 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 infrastructure technologies and apply that knowledge to production engineering problems.
Preferred Qualifications:
• Experience with Azure Kubernetes Service (AKS) or large-scale Kubernetes environments.
• Experience with cluster/node provisioning, Kubernetes controllers/operators, scheduling or resource management.
• Experience operating and scaling Kubernetes-based production infrastructure.
• Experience with GPU, AI accelerator or heterogeneous compute infrastructure.
• Experience with Linux, containers, networking and host-level system software.
• Knowledge of accelerator virtualisation and resource isolation concepts.
• Experience with infrastructure automation, CI/CD and configuration/deployment systems.
• Experience with telemetry, metrics, distributed tracing, observability and production diagnostics.
• Experience applying AI-assisted development to code generation, code comprehension, testing, troubleshooting, documentation and engineering automation.
• Experience using AI-enabled workflows to investigate complex distributed-system and infrastructure failures.
• Experience leveraging AI to automate repetitive infrastructure engineering and operational tasks.
• Ability to use AI-assisted learning alongside engineering fundamentals to accelerate development of Kubernetes, cloud infrastructure and accelerator-domain expertise.
• Experience sharing reusable AI-assisted engineering workflows and practices across an engineering team.
#AIIINFRA
This position will be open for a minimum of 5 days, with applications accepted on an ongoing basis until the position is filled.
Microsoft is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to age, ancestry, citizenship, color, family or medical care leave, gender identity or expression, genetic information, immigration status, marital status, medical condition, national origin, physical or mental disability, political affiliation, protected veteran or military status, race, ethnicity, religion, sex (including pregnancy), sexual orientation, or any other characteristic protected by applicable local laws, regulations and ordinances. If you need assistance with religious accommodations and/or a reasonable accommodation due to a disability during the application process, read more about requesting accommodations.
Skills Required
- Bachelor's degree in Computer Science, Computer Engineering, or a related technical discipline, or equivalent experience with 8+ years of relevant industry experience.
- Strong 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, containerization, and cloud-native technologies.
- Strong understanding of scalability, concurrency, state management, resiliency, and failure recovery.
- Experience developing reliable production software and debugging complex distributed systems.
- Strong design, problem-solving, and cross-team collaboration skills.
- Demonstrated ability or strong experience using AI-assisted software engineering tools and workflows.
- Ability to rapidly develop expertise in complex infrastructure technologies and apply it to production engineering problems.
- Experience with Azure Kubernetes Service or large-scale Kubernetes environments.
- Experience with cluster or node provisioning, Kubernetes controllers/operators, scheduling, or resource management.
- Experience operating and scaling Kubernetes-based production infrastructure.
- Experience with GPU, AI accelerator, or heterogeneous compute infrastructure.
- Experience with Linux, containers, networking, and host-level system software.
- Knowledge of accelerator virtualization and resource isolation concepts.
- Experience with infrastructure automation, CI/CD, and configuration or deployment systems.
- Experience with telemetry, metrics, distributed tracing, observability, and production diagnostics.
- Experience applying AI-assisted development to code generation, testing, troubleshooting, documentation, and engineering automation.
- Experience using AI-enabled workflows to investigate distributed-system and infrastructure failures.
- Experience leveraging AI to automate repetitive infrastructure engineering and operational tasks.
- Ability to use AI-assisted learning to accelerate Kubernetes, cloud infrastructure, and accelerator-domain expertise.
- Experience sharing reusable AI-assisted engineering workflows and practices across an engineering team.
Microsoft Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Microsoft and has not been reviewed or approved by Microsoft.
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Fair & Transparent Compensation — Pay is presented as broadly competitive overall, with clear role/level/location variation and an emphasis on using posted ranges and band information for apples-to-apples comparisons.
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Retirement Support — Retirement benefits are described as a standout, highlighted by a strong 401(k) match structure and immediate vesting, plus additional plan features for tax-advantaged saving.
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Parental & Family Support — Family-oriented benefits are portrayed as a meaningful strength, with substantial paid parental leave and added supports like back-up care and adoption/surrogacy assistance.
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What We Do
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