👋 Hi, I'm Varun Rajput
🧠 Platform Engineer & SRE specializing in ML Infrastructure
medium.com/@thevarunfreelance
🛠️ While my business cards have often read "DevOps Engineer," I've worn many different hats 🎩 throughout my 9+ year career—🏗️ Platform Engineer, 🛡️ Site Reliability Engineer, and now moving to 🤖 MLOps Specialist. Why so many roles? Because building truly reliable systems requires more than following a single job description. ☁️
At companies like H2O.ai, X-Team, Amazon, and EPAM, I've architected and maintained large-scale AWS & Kubernetes platforms, consistently achieving ⬆️ 99.9% production uptime across complex environments. My engineering philosophy has always centered on 🔄 automation, 👁️ observability, and 🔒 reliability—skills that have proven invaluable as I've transitioned into the ML infrastructure space.
🚀 Today, I'm focused on bringing 🏗️ platform engineering excellence to 🧠 ML and AI, where reliable infrastructure meets machine learning. My unique advantage? I bring ⚡ battle-tested reliability engineering principles to AI systems, ensuring they're not just intelligent, but also 📈 scalable, 📊 observable, and 🛡️ enterprise-ready.
🔍 My Current Focus & Core Expertise
🧠 Current Focus: MLOps & AI Infrastructure
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Adapting platform engineering principles to ML workloads
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Building specialized infrastructure for LLM deployments
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Exploring custom Kubernetes operators for AI workload management
🏗️ Infrastructure & Platform Engineering
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Infrastructure as Code with Terraform and AWS CDK
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Multi-account AWS architecture and networking
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Large-scale Kubernetes cluster management (EKS, Karpenter)
🛡️ Site Reliability Engineering
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Maintaining 99.9% uptime across production environments
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Comprehensive observability with OpenTelemetry, Prometheus, and Grafana
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Implementing SLOs and automated reliability reporting
🔄 Continuous Delivery & GitOps
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ArgoCD deployment patterns and GitOps workflows
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Production-grade Helm chart development
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Automated CI/CD pipelines with GitHub Actions and Jenkins
🛠️ Personal Projects
Custom Kubernetes Operator for LLM Deployments:
Ever experienced the frustration of deploying ML models to production? After days of back-and-forth, numerous failed deployments, and mounting frustration, I created a solution that makes LLM deployments repeatable and standardized:
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Built custom Kubernetes CRDs specifically for language model workloads
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Implemented auto-scaling based on inference demands
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Reduced deployment time from days to minutes
GitOps Framework for ML Models
A comprehensive guide and implementation for containerizing and deploying language models:
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Docker containerization best practices for ML models
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ArgoCD deployment patterns for GitOps-driven model releases
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CI/CD pipeline templates for continuous model deployment
Automated 3-Tier Infrastructure
One-Command Infrastructure Deployment
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Terraform modules for secure, cost-effective AWS infrastructure
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Automated deployment of frontend, backend, and database tiers
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Built-in security controls and best practices
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Reduced setup time from days to minutes
🚀 Tech Stack Highlights
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Container Orchestration: Kubernetes, EKS, Karpenter, Helm, ArgoCD, Istio
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Cloud Infrastructure: AWS (EKS, Lambda, Step Functions, Transit Gateway)
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IaC & GitOps: Terraform, AWS CDK, GitHub Actions, Jenkins, AzureDevOps
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Observability: OpenTelemetry, DataDog, ELK Stack, Prometheus, Grafana
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Languages: Python, Bash, Go
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Security: AWS GuardDuty, SecurityHub, CMK implementation
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ML Platforms: TensorFlow, PyTorch, SageMaker
🔗 Let's Connect!
I'm passionate about building the next generation of infrastructure for AI systems. Whether you're looking to optimize your ML pipelines, automate model deployments, or enhance your infrastructure with AI capabilities, I'd love to connect!
"The future belongs to those who bridge the gap between data science and infrastructure engineering."