Consulting

Consulting Services

I offer specialized consulting services for organizations looking to build, optimize, and scale their GPU infrastructure on Kubernetes. With my experience as Engineering Lead at Snowflake, Cloud Native Lead at Truera, and Senior Engineering Kubernetes Manager at Rakuten, I bring deep technical expertise and practical insights to help you succeed.

Services

GPU Infrastructure Design & Architecture

Design a robust GPU infrastructure strategy tailored to your AI/ML workloads:

  • Multi-GPU Kubernetes Architecture: Design scalable K8s clusters with GPU pooling and scheduling
  • MIG (Multi-Instance GPU) Strategy: Optimize GPU utilization with NVIDIA MIG technology
  • Hybrid Cloud GPU Deployments: Architect GPU solutions across on-premises, GCP, AWS, and Microsoft Azure
  • Azure GPU Expertise: AKS GPU support, Azure Machine Learning, and Azure Batch AI
  • Vendor-Agnostic GPU Support: Design for NVIDIA, AMD, and ARM GPU compatibility

Kubernetes for AI/ML Workloads

Optimize your Kubernetes platform for machine learning and AI workloads:

  • GPU Operator Implementation: Deploy and configure GPU Operators for automated GPU management
  • Device Plugins & Scheduler Integration: Extend K8s for custom GPU scheduling and resource management
  • Container Runtime Optimization: Configure containerd, CRI-O, or Kata Containers for GPU workloads
  • Distributed Training Support: Enable efficient distributed training across multiple GPUs and nodes

Performance & Cost Optimization

Maximize ROI from your GPU investments:

  • GPU Utilization Analysis: Identify and eliminate GPU fragmentation and underutilization
  • Right-Sizing GPU Infrastructure: Match GPU types and configurations to your workload requirements
  • Spot/Preemptible GPU Strategies: Implement cost-effective GPU procurement strategies
  • Resource Quota & Policy Design: Implement fair-share policies for multi-tenant GPU clusters

Cloud Native AI Strategy

Align your AI platform with cloud native best practices:

  • Cloud Native AI Working Group Guidance: Leverage CNCF best practices and reference architectures
  • TAG-Runtime Expertise: Design patterns integrating GPUs with cloud native runtimes
  • Open Source Selection: Evaluate and integrate CNCF projects for AI infrastructure
  • Platform Engineering: Build internal platforms for data science and ML engineering teams

Team & Process Enablement

Build internal capabilities to manage your GPU infrastructure:

  • Team Structure & Hiring: Design effective platform and SRE teams for AI infrastructure
  • Runbook Development: Create operational procedures for GPU cluster management
  • Training & Knowledge Transfer: Upskill your team on GPU and Kubernetes best practices
  • Incident Response: Establish processes for GPU-specific incidents and performance issues

Distributed Systems & Data Architecture

Design scalable distributed systems for data-intensive applications:

  • Large-Scale Distributed Architecture: Design systems that handle petabytes of data efficiently
  • Data Pipeline Design: Build robust ETL/ELT pipelines for ML data processing
  • Distributed Training Architectures: Enable efficient model training across multiple GPUs, nodes, and regions
  • Consensus & Coordination: Implement distributed coordination patterns (Raft, Paxos, etcd)
  • Data Sharding Strategies: Design intelligent data partitioning for distributed workloads
  • Fault-Tolerant Systems: Build resilient systems that handle partial failures gracefully

Why Work With Me

Proven Track Record

  • Engineering Lead at Snowflake | Leading GPU infrastructure and Kubernetes platform teams
  • Senior Engineering Kubernetes Manager at Rakuten | Managing Kubernetes infrastructure at scale
  • Cloud Native Lead at Truera | Building cloud native AI/ML platforms
  • CNCF Technical Oversight Committee (TOC) | Governing cloud native infrastructure projects
  • PyTorch Technical Advisory Council (TAC) | Shaping PyTorch Foundation direction (2025)
  • CNCF TAG-Runtime Co-Chair | Shaping container runtime standards
  • Multi-Cloud Expertise | Deep experience across GCP, AWS, and Microsoft Azure

Deep Technical Expertise

  • GPU Operator Development: Contributed to KEP-3093 for Kubernetes GPU enhancements
  • PyTorch Foundation Leadership: PyTorch Technical Advisory Council member
  • Container Runtime Leadership: containerd maintainer, Kata Containers contributor
  • Multi-Vendor GPU Support: Experience with NVIDIA, AMD, and ARM GPUs
  • Distributed Systems: Designed systems handling petabyte-scale data
  • Multi-Cloud Architecture: Deep expertise across GCP, AWS, and Microsoft Azure
  • Production at Scale: Managed infrastructure supporting millions of users

Thought Leadership

  • KubeCon Keynote Speaker | Multiple KubeCon and CNCF conference talks
  • Cloud Native AI Whitepaper Lead Author | Defining industry best practices
  • Open Source Contributor | Active contributor to CNCF projects
  • Industry Recognition | Regular speaker at major cloud native conferences

Engagement Models

Advisory & Strategy

  • Executive Briefings: Strategic guidance on AI infrastructure direction
  • Architecture Reviews: Deep-dive reviews of existing GPU infrastructure
  • Technology Selection: Vendor and technology stack recommendations

Hands-On Implementation

  • Proof of Concept: Rapid validation of GPU infrastructure approaches
  • Production Deployments: End-to-end implementation support
  • Performance Tuning: Optimization of existing GPU/K8s deployments

Training & Enablement

  • Custom Workshops: Tailored training for your engineering teams
  • Office Hours: Ongoing advisory and troubleshooting support
  • Documentation: Runbooks, playbooks, and best practice guides

Get In Touch

Ready to optimize your GPU infrastructure? Let’s discuss your challenges and goals.

Interested in working together?

Contact Me

to schedule a consultation about your GPU infrastructure needs.

consulting  gpu  kubernetes  ai  infrastructure  pytorch  distributed-systems