Scalable Kubernetes and GPU infrastructure for enterprise AI workloads.
Operating cloud-native AI/ML infrastructure across GCP, AWS, and Snowflake, deploying high-availability GKE clusters with GPU acceleration, vector databases, and microVM isolation.
PLATFORM ENGINEER / QUANTIPHI
50+
AI microservices supported
GCPGKEFIRECRACKERQDRANTMILVUSTERRAFORMGPU L4/T4
01 / PROBLEM & CONTEXT
Scaling production AI workloads reliably.
Production AI applications require complex combinations of GPU acceleration, vector indexing, low-latency execution isolation, and high-throughput model serving.
02 / MY ROLE & OWNERSHIP
Platform Engineer / Quantiphi
Built and maintained the underlying Kubernetes clusters, GPU node pools, and vector database deployments supporting enterprise AI products.
Engineered Kubernetes platforms using GKE with GPU L4/T4 node pools
Managed distributed Qdrant and Milvus vector databases for AI search and retrieval
Integrated Firecracker microVM isolated execution boundaries for AI agent tasks
Implemented centralized observability with Prometheus, Grafana, and Cloud Monitoring
06 / RESULT
50+
AI Microservices in production
Resilient infrastructure for enterprise AI.
Supported over 50 production AI microservices with 99.9% platform uptime.
Supported 50+ AI microservices
99.9% platform uptime
Zero-downtime rolling deployments
More systems. Same intent.