The Azure Compute team is looking to solve the problem of ensuring every Azure VM achieves an SLA of 99.99+%. This requires out-of-the-box thinking, data-driven decisions, and intelligent automation to monitor the health of millions of Azure machines and make repair decisions. The team is also exploring the use of generative AI to enhance diagnostics, automate root cause analysis, and accelerate incident resolution.
Requirements
- 3+ years technical engineering experience with coding in languages including, but not limited to, Rust, C, C++, C-Sharp, Java, JavaScript, or Python
- 1+ years of experience in designing, building and shipping high quality production software or services in cloud environment.
- 1+ years of experience in large-scale distributed systems analysis and troubleshooting.
- 1+ years of experience in high-stakes production online service environments.
- Demonstrated ability and passion for designing and building highly available distributed systems at scale.
- Demonstrated problem solving and debugging skills.
- Demonstrated ability to exercise sound judgment in ambiguous situations.
Responsibilities
- Leads the design and architecture of change management features and services in Azure Compute
- Develops high quality, extensible, maintainable code and coaches others to do the same.
- Supports livesite as Designated Responsible Individual (DRI), mentoring engineers across products/solutions, working on-call to monitor system/product/service for degradation, downtime, or interruptions.
- Collaborates with data scientists and ML engineers to design and integrate predictive models that proactively detect hardware anomalies and trigger live migrations, improving VM uptime and SLA compliance.
- Leads initiatives to embed AI-driven diagnostics and root cause analysis into availability services, reducing time-to-resolution for incidents and improving operational efficiency.
- Drives the adoption of generative AI tools to automate documentation, incident summaries, and engineering workflows, enhancing team productivity and knowledge sharing.
- Partners with platform teams to build intelligent observability pipelines that leverage anomaly detection and trend analysis for early warning systems.
Other
- Partners with appropriate stakeholders spanning across teams and orgs to determine project requirements
- Leverages expertise with appropriate stakeholders to develop project plans, release plans, and work items.
- Proactively seeks new knowledge and adapts to new trends, technical solutions, and patterns that will improve the availability, reliability, efficiency, observability, and performance of products while also driving consistency in monitoring and operations at scale and shares knowledge with other engineers.
- Evaluates and integrates large-scale AI models into control plane services to enable smarter, context-aware repair decisions across millions of Azure VMs.
- Experience with agile methodologies is a plus, a willingness to adopt them is required.
