• Reliability & Availability: Ensure uptime, resiliency, and fault tolerance of AI model training and inference systems.
• Observability: Design and maintain monitoring, alerting, and logging systems to provide real-time visibility into model serving pipelines and infra.
• Performance Optimization: Analyze system performance and scalability, optimize resource utilization (compute, GPU clusters, storage, networking).
• Automation & Tooling: Build automation for deployments, incident response, scaling, and failover in hybrid cloud/on-prem CPU+GPU environments.
• Incident Management: Lead on-call rotations, troubleshoot production issues, conduct blameless postmortems, and drive continuous improvements.
• Security & Compliance: Ensure data privacy, compliance, and secure operations across model training and serving environments.
• Collaboration: Partner with ML engineers and platform teams to improve developer experience and accelerate research-to-production workflows.

Required Qualifications

• 4+ years of experience in Site Reliability Engineering, DevOps, or Infrastructure Engineering roles.

Preferred Qualifications

• Strong proficiency in Kubernetes, Docker, and container orchestration.
• Knowledge of CI/CD pipelines for Inference and ML model deployment.
• Hands-on experience with public cloud platforms like Azure/AWS/GCP and infrastructure-as-code.
• Expertise in monitoring & observability tools (Grafana, Datadog, OpenTelemetry, etc.).
• Strong programming/scripting skills in Python, Go, or Bash.
• Solid knowledge of distributed systems, networking, and storage.
• Experience running large-scale GPU clusters for ML/AI workloads (preferred).
• Familiarity with ML training/inference pipelines.
• Experience with high-performance computing (HPC) and workload schedulers ( Kubernetes operators).
• Background in capacity planning & cost optimization for GPU-heavy environments.
• Work on cutting-edge infrastructure that powers the future of Generative AI.
• Collaborate with world-class researchers and engineers.
• Impact millions of users through reliable and responsible AI deployments.
• Competitive compensation, equity options, and comprehensive benefits.

Software Engineering IC4 - The typical base pay range for this role across the U.S. is USD $119,800 - $234,700 per year. There is a different range applicable to specific work locations, within the San Francisco Bay area and New York City metropolitan area, and the base pay range for this role in those locations is USD $158,400 - $258,000 per year.

Certain roles may be eligible for benefits and other compensation. Find additional benefits and pay information here:
https://careers.microsoft.com/us/en/us-corporate-pay

Software Engineering IC5 - The typical base pay range for this role across the U.S. is USD $139,900 - $274,800 per year. There is a different range applicable to specific work locations, within the San Francisco Bay area and New York City metropolitan area, and the base pay range for this role in those locations is USD $188,000 - $304,200 per year.

Certain roles may be eligible for benefits and other compensation. Find additional benefits and pay information here:
https://careers.microsoft.com/us/en/us-corporate-pay

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.