AI-ready systems • CCNP (Enterprise + Automation) • NVIDIA AI Infrastructure
I build reliable infrastructure systems that scale under pressure. My focus is on network reliability, observability pipelines, and security systems thinking. I work at the intersection of AI-ready networking, automation, and operational resilience—designing systems that handle failures gracefully and optimize resources efficiently.
From operating enterprise L2/L3 networks to building AI-aware traffic optimizations and GPU observability correlators, I approach infrastructure as a cohesive system where compute, fabric, and defense layers must work in harmony.
Project-based infrastructure + automation work under NDA; AI-ready networking, observability, failure analysis, cost optimization.
AI training workloads create unpredictable traffic patterns leading to congestion and latency spikes.
Traffic simulation engine analyzing GPU communication patterns, predicting congestion points, and optimizing routing decisions.
Reduced latency spikes under simulated peak load through predictive traffic shaping experiments.
Python • Network Simulation • Traffic Analysis • BGP
Performance degradation in GPU clusters often stems from network or storage issues, not compute itself.
Multi-layer observability pipeline correlating GPU utilization, network throughput, and workload characteristics.
Improved incident pinpointing under load by correlating infrastructure and compute signals.
Prometheus • Grafana • NVIDIA SMI • Network Telemetry
Edge inference nodes face unreliable connectivity and bandwidth constraints while requiring consistent SLAs.
Adaptive inference system with intelligent model caching, request batching, and graceful degradation strategies.
Kept service responsive in high loss simulations using resilient edge and routing policies.
ONNX Runtime • Redis • Network QoS • Failover Logic
Traditional security tools generate false positives on AI workload traffic patterns (burst transfers, large payloads).
ML-based anomaly detection system trained on legitimate AI traffic patterns with context-aware alerting.
Lowered noisy alerts in validation runs while surfacing real threats on AI traffic patterns.
Zeek • Suricata • Machine Learning • Flow Analysis
Teams struggle to identify root causes quickly during outages due to lack of failure pattern knowledge.
Chaos engineering platform for systematic failure injection with automated symptom cataloging and playbooks.
Reduced average incident resolution time from 4 hours to 45 minutes through documented failure patterns.
Chaos Mesh • Kubernetes • Terraform • Runbooks
Cloud AI infrastructure costs spiral without visibility into primary cost drivers and optimization opportunities.
Cost attribution system mapping workload characteristics to resource consumption with actionable optimization recommendations.
Surfaced cost drivers in modeling exercises, informing GPU sizing and network optimization choices.
FinOps • Cloud Billing APIs • Data Analysis • Optimization
Capstone: Arcane Guard — AI-Driven Security for IoT Networks
ML/DL-based intrusion detection system with real-time pipeline processing, scalable architecture, and false positive reduction through multi-stage classification.
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Interested in infrastructure reliability, AI-ready networking, or systems architecture? Let's connect.