Jagadesh - AI/ML Infrastructure Expert

About Me

Who I Am

I’m Jagadesh, a Senior Platform Engineer specializing in AI/ML infrastructure on Kubernetes. Based in St. Louis, Missouri, I help organizations build, optimize, and operate production-grade ML platforms that deliver both technical excellence and business value.

What sets me apart is my unique combination of deep Kubernetes expertise and hands-on ML infrastructure experience. Unlike specialists who focus solely on traditional workloads, I bring practical expertise in GPU orchestration and ML-specific challenges:

• Google Kubernetes Engine (GKE) with GPU node pools
• Amazon Elastic Kubernetes Service (EKS) with GPU instances
• Azure Kubernetes Service (AKS) with GPU-enabled clusters
• NVIDIA GPU Operator and MIG configuration
• Kubeflow and KServe implementations
• Custom Go operators for ML workflows

This cross-platform ML infrastructure perspective allows me to recommend and implement the right solutions for your AI/ML needs, optimized for both performance and cost.

My Expertise

AI/ML Infrastructure

• GPU Orchestration: NVIDIA GPU Operator, MIG configuration, and GPU scheduling optimization
• Model Serving Platforms: KServe, Seldon Core, and Triton Inference Server deployments
• Training Infrastructure: Kubeflow pipelines, distributed training with Horovod/PyTorch
• ML Workflow Automation: Custom operators for experiment tracking and model lifecycle
• Cost Optimization: Spot GPU strategies reducing training costs by 60-70%

Development & Platform Engineering

• Go-based ML Operators: Building production-grade operators for ML workflow automation
• Custom Controller Development: Extensive experience with operator-sdk for ML platforms
• Cloud-Native ML Services: High-performance Go services for model serving and data pipelines
• Platform Tooling: GPU monitoring, cost tracking, and utilization optimization tools

GPU Resource Management

• Utilization Optimization: Improving GPU utilization from 30% to 85%+
• Multi-Instance GPU (MIG): Maximizing inference throughput with GPU partitioning
• Time-Slicing: Enabling GPU sharing for development workloads
• Spot Instance Management: Automated checkpointing and recovery for training jobs

ML Platform Components

• Kubeflow: End-to-end ML workflow orchestration and pipeline management
• KServe/Seldon: Model serving at scale with automatic scaling and A/B testing
• MLflow: Model registry, experiment tracking, and versioning
• Ray on Kubernetes: Distributed training and hyperparameter tuning
• Jupyter Enterprise Gateway: Scalable notebook infrastructure

Platform Engineering

• Distribution-agnostic architecture: ML solutions that work across any Kubernetes distribution
• Multi-cloud ML deployments: Consistent ML platforms across AWS, GCP, and Azure
• Hybrid cloud integration: Connecting on-premise GPU resources with cloud
• Production readiness: Enterprise-grade ML platforms operational in days
• Migration strategies: Moving ML workloads between platforms with minimal disruption

Cost Optimization for ML

• GPU resource efficiency: Right-sizing GPU instances for specific model requirements
• Spot GPU strategies: 60-70% cost reduction with intelligent checkpointing
• Inference optimization: Model quantization and batching strategies
• Multi-tenant GPU sharing: Maximizing GPU utilization across teams

Developer Experience

• ML pipeline automation: Self-service model training and deployment
• Experiment tracking: Automated capture of metrics and parameters
• Model versioning: GitOps for ML models and configurations
• Development environments: GPU-enabled notebooks and IDEs

Observability & Monitoring

• GPU metrics: Utilization, memory, temperature monitoring
• Model performance: Latency, throughput, and accuracy tracking
• Training metrics: Loss curves, learning rates, and convergence monitoring
• Cost attribution: Per-team and per-model cost tracking

Services Offered

I provide specialized consulting services tailored to your organization’s ML infrastructure needs:

Strategic ML Infrastructure Assessment

• GPU utilization analysis
• ML platform architecture review
• Cost optimization opportunities
• Model serving strategy evaluation

Implementation

• Production-ready GPU clusters
• Kubeflow and KServe deployment
• Custom ML operator development
• Distributed training infrastructure

Optimization

• GPU utilization improvement
• Inference latency reduction
• Training cost optimization
• Model serving efficiency

Knowledge Transfer

• ML infrastructure best practices
• Team training on Kubernetes for ML
• Documentation development
• Operational runbooks for ML platforms

Technical Background

My technical foundation includes:

• Languages: Go, Python, Java, Bash
• ML Infrastructure: Kubeflow, KServe, Seldon, MLflow, Ray
• GPU Technologies: CUDA, NVIDIA GPU Operator, MIG, nvidia-smi
• Kubernetes Development: operator-sdk, kubebuilder, controller-runtime, client-go
• Infrastructure as Code: Terraform, Ansible, Pulumi
• Cloud Platforms: AWS (with SageMaker integration), GCP (with Vertex AI), Azure (with AzureML)
• Containerization: Docker, Containerd, NVIDIA Container Toolkit
• Service Mesh: Istio, Linkerd (for model A/B testing)
• Observability: Prometheus, Grafana, DCGM Exporter
• CI/CD: GitHub Actions, GitLab CI, Tekton, ArgoCD

Certifications

• Certified Kubernetes Administrator (CKA)
• Certified Kubernetes Application Developer (CKAD)
• Google Cloud Professional Cloud Architect
• Google Cloud Professional ML Engineer (in progress)
• AWS Certified Solutions Architect
• NVIDIA Deep Learning Institute Certifications

My Approach

I believe that ML infrastructure should accelerate innovation, not create bottlenecks. My approach emphasizes:

1. ML-first thinking: Every infrastructure decision supports model development and deployment velocity
2. Cost-conscious scaling: Balancing GPU performance with budget constraints
3. Developer empowerment: Self-service platforms that don’t compromise governance
4. Production reliability: ML systems that meet enterprise SLAs
5. Continuous optimization: Regular reviews of GPU utilization and costs

Featured ML Infrastructure Articles

The articles on this site showcase my expertise in ML infrastructure engineering:

1. Building Production-Ready AI/ML Infrastructure on Kubernetes - Complete guide to GPU orchestration, model serving, and distributed training

2. From 30% to 85%: Optimizing GPU Utilization - Practical strategies for maximizing GPU efficiency in Kubernetes

3. Multi-Cloud ML Platform Architecture - Building consistent ML infrastructure across cloud providers

4. ML Infrastructure Cost Optimization - Reducing GPU costs by 65% with spot instances and smart scheduling

5. Building ML Pipeline Operators in Go - Developing custom operators for ML workflow automation

6. Zero to Production: ML Platform in One Day - Rapid deployment of enterprise-grade ML infrastructure

Recent ML Projects

GPU Cluster Optimization for LLM Training

• Configured multi-node distributed training for 7B parameter models
• Implemented automatic checkpointing for spot instance interruptions
• Achieved 70% cost reduction while maintaining training stability

Model Serving Platform for Computer Vision

• Deployed KServe with custom transformers for image preprocessing
• Implemented model versioning with canary deployments
• Scaled to handle 100K+ inference requests per second

MLOps Platform for Financial Services

• Built end-to-end ML pipeline with Kubeflow
• Integrated with existing CI/CD for model deployment
• Implemented comprehensive model governance and audit trails

Let’s Connect

I’m always interested in discussing challenging ML infrastructure problems and innovative solutions. Whether you’re looking for consulting assistance, planning your ML platform strategy, or simply want to exchange ideas about GPU orchestration and model serving, I’d love to hear from you.

Contact me at: - Email: hello@jagadesh.dev - LinkedIn: linkedin.com/in/egntuywbw001 - GitHub: github.com/jagstack

Looking for ML infrastructure expertise? Schedule a free 30-minute consultation to discuss your GPU orchestration and model deployment challenges.