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BHARATH MADVAR

Enterprise AI & Kubernetes Platform Leader

Building multi-cloud AI foundations across GCP, Azure, and AWS with production-ready Kubernetes & MLOps.

I design resilient AI foundations that connect data science, AI engineering, and application teams. My focus spans GPU clusters, Kubernetes ecosystems, IaC/GitOps, and automated MLOps practices that keep innovation compliant, observable, and production-ready across regulated industries.

11+ years platform leadership 100+ production Kubernetes clusters GCP · Azure · AWS
CKA Terraform Associate NVIDIA AI Infrastructure Security: Vault, OPA, RBAC

Platform Credentials & Depth

IaC & GitOps

Terraform, CloudFormation, Ansible, ArgoCD, Helm, GitLab CI/CD, GitHub Actions — policy-driven pipelines for multi-cloud platforms.

Cloud Platforms

GKE, AKS, EKS, RKE2, Rancher, Vertex AI, Azure ML, AWS SageMaker — from GPU node pools to managed AI services.

Security & Governance

Vault, Cert-Manager, OPA/Gatekeeper, Workload Identity, Azure AD RBAC, PCI/HIPAA controls, encrypted service meshes.

Networking & Observability

Istio, service meshes, SR-IOV, Multus CNI, private ingress, Prometheus/Grafana, DCGM, distributed tracing for AI workloads.

Enterprise AI Infrastructure Architect Blueprint

Enterprise AI infrastructure diagram showing data sources, AI services, Kubernetes platform, and cloud connectivity

Multi-cloud AI & Kubernetes Platform

This blueprint captures the platform pattern I deliver for regulated enterprises: secure data ingestion, GPU-ready Kubernetes workloads, and federated cloud connectivity across GCP, Azure, and AWS.

  • Edge & core pipelines feeding Kubeflow/Vertex AI for experimentation.
  • Service mesh (Istio) + security (Vault, OPA, Workload Identity) controlling traffic.
  • Observability plane (Prometheus, Grafana, DCGM) across 100+ clusters.
  • Hybrid connectivity via Anthos, Private Service Connect, and encrypted APIs.

Recent Roles & Impact

Lowe's

Senior Cloud & Platform Engineer · 2021 – Present

Driving enterprise AI infrastructure, GPU-ready Kubernetes platforms, and zero-downtime modernization programs across 140+ clusters spanning GKE, Anthos, and on-prem NVIDIA DGX.

Global Payments

Senior AWS DevOps Engineer · May 2020 – Sep 2021

Automated PCI-compliant AWS environments with EKS, Terraform, CloudFormation, Kafka, Vault, and Helm; hardened GitLab/GitHub CI/CD and Prometheus/Grafana telemetry for payment APIs.

Object Computing Inc.

Senior Cloud Engineer (Consulting) · Apr 2017 – May 2020

Delivered AKS, EKS, and RKE2 platforms for public-sector and healthcare clients using ARM templates, Azure DevOps, ArgoCD, and multi-tenant Kubeflow pipelines across hybrid clouds.

North Dakota Dept. of Health

Consult Engagement via OCI · St. Louis, MO

Modernized HIPAA-governed data platforms with Kubeflow, KServe, encrypted Istio meshes, and Azure AD-backed RBAC for statewide health teams.

Mastercard

Platform Engineer · May 2016 – Apr 2017

Engineered resilient payment infrastructure, codified Terraform-based provisioning, and embedded compliance automation with Vault, Consul, and multi-region service meshes.

AI Platform Highlights

GPU Video Analytics Platforms

Technical Lead for two enterprise AI/ML initiatives on GPU-enabled Kubernetes: real-time warehouse safety monitoring and in-store customer assistance with privacy-aware computer vision.

  • Forklift & pallet tracking, employee safety compliance, high-res camera telemetry.
  • Consent-based facial recognition, personalized recommendations, and realtime AI copilots for store associates.
  • Built scalable RTSP ingestion (GStreamer, FFmpeg, DeepStream) with GPU buffering and burst resiliency.

Kubeflow + Vertex AI Platform

Integrated Kubeflow with GPU-enabled node pools, RBAC, namespace isolation, Istio ingress, object storage, and distributed training operators, complemented by managed Vertex AI services.

  • Autoscaled inference and batch prediction on A100, H100, L4, and T4 GPU tiers with versioned rollouts.
  • Traffic splitting, model lineage, and GPU autoscaling for mission-critical workloads.
  • Distributed training using PyTorch DDP, Horovod, and MPI across on-prem NVIDIA DGX racks.

Observability & Networking for AI

Built observability stacks and high-performance Kubernetes networking tailored for GPU-heavy AI systems across retail, payments, and healthcare environments.

  • Unified logging, tracing, and GPU telemetry (Prometheus, Grafana, DCGM) for 140+ clusters.
  • Multi-tenant Istio meshes, private ingress, and RDMA networking for low-latency inference.
  • HIPAA-compliant data paths, encrypted storage, and policy-driven governance for regulated workloads.

Business Impact

This platform enables data science, AI engineering, and application teams to rapidly build, train, deploy, and operate machine learning and generative AI workloads while maintaining enterprise requirements for scalability, security, governance, observability, and operational reliability. It serves as a reusable AI foundation capable of supporting model development, distributed training, real-time inference, and large-scale production AI deployments across multiple business domains.

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Rapid Development

Accelerate AI model development and deployment with streamlined workflows and automated pipelines.

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Enterprise Security

Maintain enterprise-grade security, governance, and compliance across all AI workloads.

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Scalability

Scale from prototype to production with infrastructure that grows with your business needs.

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Real-time Inference

Deploy models for real-time inference with low latency and high throughput.

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Observability

Gain comprehensive insights into model performance and system health.

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Production Ready

Built for large-scale production AI deployments with operational reliability.