Architected Heterogeneous Compute Platform: abstracted heterogeneous compute (dedicated GPU + serverless CPU pools) across multiple Kubernetes clusters into a single resource substrate via recursive K8s-on-K8s virtualization (virtual-node-on-virtual-node); enabled Serverless-to-Reserved migration delivering 25x capacity scaling per availability zone at ~40% TCO reduction — the foundational compute layer all downstream SaaS depends on.
Engineered Cross-Cluster Identity Mesh: federated multiple Kubernetes clusters via application-layer routing + per-pod secrets-mount credential delivery; remote pods authenticate to customer VPC and private container registry transparently without static credentials.
Built AI Dev Workstations as a 0→1 SaaS product: pioneered dual-plane networking enabling simultaneous customer-VPC private egress AND public-internet developer ingress on the same pod — a capability the underlying cloud network model did not natively support; provisions in sub-60s p95 vs days of DIY VPC-peering + bastion setup.
Built Distributed Training & Simulation Scheduler as a 0→1 SaaS product: abstracted mixed CPU/GPU training pipelines into a clean job schema; per-component dispatch routes GPU-heavy workers to dedicated hardware and CPU-heavy workers to serverless infrastructure, shielding users from compute heterogeneity while delivering ~30-40% cost reduction on mixed workloads.
Architected Supervisor-Worker Multi-Agent System with 4-Gate Hallucination Defense: separated planning from execution via a supervisor dispatching to specialist sub-agents; new-skill additions gated by pass^3 ≥ 80% (Anthropic τ-bench consistency metric) on the regression suite — surfaces non-deterministic LLM failures missed by single-run tests.
Designed Meta-Tool with Hierarchical Skill Tree: agents perform multi-round retrieval over a 3-layer skill tree, narrowing the candidate set at each descent; yields avg 6 tools loaded per invocation out of 200+ available (~3% surface), invariant to catalog growth.
GPU Lifecycle & Blocklisting: Architected scalable tracking system handling thousands of concurrent scaling requests; designed parent-child DAG in Apache Airflow; eliminated circular termination loops, delivering multi-million dollar annualized savings.
Automated Reliability Orchestrator: Designed fault identification workflow for large-scale GPU clusters using divide-and-conquer with dynamic K8s node labeling; reduced troubleshooting time by ~90% per incident.
API & Monitoring: Built customer-facing API/CLI (Lambda, Smithy); optimized DynamoDB O(N) → O(log N), improving query response latency by over 70%.
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