This list contains only the countries for which job offers have been published in the selected language (e.g., in the French version, only job offers written in French are displayed, and in the English version, only those in English).
Azure Resource Graph’s (ARG, part of ARM/Azure Control Plane) mission is to reliably organize planet-scale cloud inventory and make it accessible to every Azure customer through scalable and performant tools. ARG is the inventory backbone for Azure resources, dependencies, services, applications, and metadata. It powers core Azure experiences like Azure Portal, Marketplace, Search, and Catalog at scale for every single customer. ARG provides a unified query layer over the cloud via rich, efficient constructs used by enterprises and internal Microsoft teams alike. Our vision is to enable every customer—from startups to global enterprises—to organize, govern, secure, and manage their cloud environment at scale. We return 6.5+ billion resources per day, forming a foundational piece of Microsoft's cloud infrastructure. As a Sr. Software Engineer you will be part of the core engineering team, you will help evolve ARG’s platform and play a pivotal role in building a connected and intelligent inventory layer that enables insights and automation across Azure.
Job Responsibility:
Drive critical platform initiatives to scale with Azure’s growing needs, focusing on building high-throughput ingestion and querying infrastructure for cloud metadata and graph relationships
Own technical design, implementation, and delivery of systems that span distributed services, APIs, ingestion pipelines, and scalable storage
Contribute to performance tuning across the stack—from gateway services to runtime query engines—ensuring responsiveness and efficiency at large data volumes
Collaborate with engineers across Azure Core to ensure secure, reliable, and observable service operation at global scale
work on integrating AI-assisted capabilities (e.g., auto-tagging, anomaly detection, smart recommendations) into resource inventory using machine learning pipelines or inference APIs
Contribute to intelligent search and natural language querying features by partnering with teams working on Azure OpenAI and Azure Cognitive Search
Grow technical depth in distributed systems, runtime internals, and optional database technologies, while contributing reusable building blocks across the platform
Mentor other engineers, uphold engineering best practices, and foster a collaborative, high-trust team environment and understand and evolve platform architecture with a balance of short-term delivery and long-term sustainability
Requirements:
Bachelor's Degree in Computer Science or related technical field AND 4+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, OR Java, JavaScript, or Python OR equivalent experience
3+ years of experience building and shipping large scale cloud services such as Azure, AWS or Google Cloud
Ability to meet Microsoft, customer and/or government security screening requirements
Microsoft Cloud Background Check
Nice to have:
Bachelor's Degree in Computer Science OR related technical field AND 8+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, OR Python OR Master's Degree in Computer Science or related technical field AND 6+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python OR equivalent experience
4+ years of experience designing, building, and operating cloud-scale services or distributed systems
Proficient in object-oriented programming (C#, Java, or similar), including runtime-level understanding and performance optimization
2+ years of hands-on experience running services in a major public cloud (Azure, AWS, or GCP), with a focus on reliability and operational excellence and understanding of computer science fundamentals (data structures, algorithms, concurrency, system design)
1+ years of experience in AI infrastructure internals or applied AI applications