Scope#
Operated and improved a cloud AI infrastructure platform serving AI-native teams and neo-cloud providers reselling excess GPU capacity. Focus areas included Kubernetes operations, production reliability for training and inference workloads, incident handling, observability, and automation across multi-provider environments.
What I Owned#
- Platform operations across providers: Provisioned, configured, and operated Kubernetes clusters and containerized workloads across heterogeneous infrastructure.
- Automation of infrastructure workflows: Built internal tooling for cluster provisioning, integration, and lifecycle management to reduce manual operational load.
- Reliability engineering for AI workloads: Drove durable reliability and scalability improvements for both training and inference paths, prioritizing systemic fixes over one-off recoveries.
- Observability and alerting: Implemented and refined monitoring, health checks, and alerting signals to detect regressions and customer-impact risk earlier.
- Incident response and follow-through: Investigated and resolved customer-facing incidents across networking, storage, scheduling, and system layers, then fed findings into postmortems, runbooks, and prevention work.
- Operational ownership and cross-functional execution: Participated in on-call, improved escalation and day-to-day operating cadence, and partnered with engineering and product to deliver new infrastructure capabilities safely.
Reliability Outcomes#
- Increased operational maturity by standardizing runbooks, escalation flow, and post-incident follow-up for production AI infrastructure.
- Improved detection and triage readiness through stronger monitoring and alert coverage on critical service paths.
- Reduced repeat operational toil by automating core cluster lifecycle and integration tasks.
- Strengthened resilience for customer workloads by addressing failure modes across the full stack instead of applying temporary mitigations.
- Improved production support for neo-cloud GPU resale customers by stabilizing the infrastructure layers their training and inference workloads depended on.
