Jagadesh - AI/ML Infrastructure Expert

AI/ML Infrastructure Engineering: GPU-Optimized Kubernetes at Scale

Enterprise-Grade ML Platforms: From Training Pipelines to Model Serving

I help organizations build and scale AI/ML infrastructure on Kubernetes, specializing in GPU orchestration, model serving, and distributed training. With deep expertise in Go, Kubernetes, and cloud-native practices, I deliver production-ready ML platforms that handle everything from distributed training to high-throughput inference.

Schedule a Free ML Infrastructure Consultation | View My Services

Why Work With Me?

GPU & ML Infrastructure Expertise

Deep experience with NVIDIA GPU operators, MIG configuration, and optimizing GPU utilization from 30% to 85%+ for ML workloads.

Go Development for ML Ops

I leverage Go’s performance to build custom operators and controllers that automate ML workflows, manage model deployments, and orchestrate training pipelines.

Cross-Platform ML Solutions

Hands-on experience deploying ML infrastructure across GKE, EKS, AKS with GPU node pools, supporting everything from experimentation to production inference.

Proven GPU Cost Reduction

I’ve helped enterprises reduce ML infrastructure costs by 60-70% through spot GPU strategies, time-slicing, and intelligent workload scheduling.

Rapid ML Platform Implementation

Using battle-tested patterns with Kubeflow, KServe, and custom operators, I can establish production ML platforms in days, not months.

Services

ML Infrastructure Assessment

Expert evaluation of your current ML platform capabilities and GPU utilization, with actionable recommendations for optimization. Learn More

GPU Cluster Implementation

End-to-end setup of GPU-enabled Kubernetes clusters optimized for ML workloads, including NVIDIA operators, monitoring, and autoscaling. Learn More

Model Serving Platform

Production-ready model serving infrastructure with KServe/Seldon, supporting thousands of models with automatic scaling and A/B testing. Learn More

Training Pipeline Automation

Implement Kubeflow pipelines or custom training orchestration with distributed training support, spot instance management, and automatic checkpointing. Learn More

Custom ML Operators

Go-based operator development for automating ML workflows, model lifecycle management, and experiment tracking. Learn More

ML Infrastructure Case Studies

Financial Services: Real-time Fraud Detection

Challenge: Deploy 50+ ML models with sub-100ms latency requirements
Solution: KServe with GPU inference endpoints, intelligent batching, and model caching
Result: 50ms P99 latency, 10x throughput increase, 70% cost reduction

Healthcare: Medical Imaging Pipeline at Scale

Challenge: Process 1M medical images daily for diagnostic models
Solution: Distributed GPU processing with Kubeflow pipelines and spot instances
Result: Reduced processing time from 24 hours to 2 hours, 60% cost savings

E-commerce: Recommendation System Platform

Challenge: Serve personalized recommendations to 10M users with <20ms latency
Solution: Multi-model serving with Triton Inference Server and A/B testing framework
Result: 15ms average latency, 3x conversion improvement

Let’s Build Your ML Infrastructure

Whether you’re starting your ML journey, struggling with GPU utilization, or ready to scale to production, I can help you build robust, cost-effective ML infrastructure on Kubernetes.

Schedule a Free 30-Minute ML Infrastructure Consultation

About Me | Services | Articles | Contact