Shiv Reddy
Enterprise Cloud · DevOps · Kubernetes · IAC · CI/CD Expert
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Shiv is ready for their next project!
Strong piece — Kubernetes/EKS is one of the highest-demand skills for your target clients, so this belongs in your portfolio. Here's the tightened, client-facing version:
Kubernetes Portfolio Application — Spring Boot on EKS
Production-grade containerized app on Amazon EKS, fully automated with Terraform, Docker, ECR & Helm
A Spring Boot application deployed to Amazon EKS with auto-scaling, load balancing, and high availability — demonstrating end-to-end DevOps: infrastructure automation, cloud-native deployment, and Kubernetes best practices.
What It Demonstrates
End-to-end automation — source code to production deployment, nothing manual
Infrastructure as Code — complete EKS environment provisioned via modular Terraform
Enterprise Helm charts — templated, configurable, reusable Kubernetes deployments
Horizontal Pod Autoscaling — dynamic scaling on CPU metrics
High availability — multi-zone deployment with LoadBalancer failover
Multi-architecture Docker builds — ARM64 (Mac M-series) and AMD64 (EKS nodes)
Production readiness — health checks, monitoring, and observability built in
Why It Matters
This is the full production Kubernetes lifecycle clients actually need: containerize, automate the infra, deploy with Helm, scale automatically, stay available across zones. The same patterns apply whether it's one app or a fleet of microservices.
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Terraform AWS Portfolio Infrastructure
Production-grade AWS infrastructure, fully automated with Terraform
The live infrastructure behind my portfolio site (shivdevops.cloud (http://shivdevops.cloud)), built entirely as code — demonstrating modular design, automation, and cloud-native best practices you'd apply to any production environment.
What It Shows
Modular Terraform structure — reusable, maintainable, environment-ready
Automated provisioning of AWS resources with clean state management
Infrastructure as code done right: reproducible, version-controlled, documented
Cloud-native best practices — security, scalability, and cost-awareness built in
Why It Matters
This is how I build infrastructure for clients: nothing manual, nothing undocumented. Every resource defined in code, every change reviewable, the whole environment reproducible from scratch. The same approach scales from a portfolio site to enterprise production.
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SRE Automation & AI Chatbot Platform
Enterprise-grade resource optimization + AI-powered log analysis
Automated EC2 scaling with predictive analytics, intelligent approval workflows, and Bedrock-powered log intelligence — built to cut cloud waste and manual toil.
What It Solves
Forecasts resource needs from 24h metrics using predictive analysis, so scaling happens before problems hit
AI-powered log analysis (AWS Bedrock / Claude) — ask plain-English questions, get answers from your logs
Approval workflows for governance — no unsupervised changes to production
Safe automatic scaling inside maintenance windows, with real-time SNS notifications
Complete audit trails in DynamoDB for compliance
How It's Built
Terraform-managed infrastructure with S3 backend + DynamoDB state locking
GitLab CI/CD pipeline: validate → plan → apply with manual approval gates
SRE Agent Lambda monitoring metrics hourly, with predictive forecasting
Approval via Parameter Store, execution in scheduled maintenance windows
AI chatbot pulling from CloudWatch & S3, with response caching to control cost
Impact
Turns reactive, manual scaling into predictive, governed automation
Makes log analysis self-service — engineers ask questions instead of grepping logs
Every action auditable, every change approved — production-safe by design
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AI Developer Agent — Linear + GitHub + AWS Fargate
Assign a ticket. Get a PR. Zero human involvement.
A production AI developer agent that reads Linear tickets, analyzes your codebase, writes production-ready code, and opens GitHub PRs — fully automated using Claude Code CLI on AWS Fargate.
What It Does
Assign a ticket in Linear → agent analyzes the codebase, posts a detailed implementation plan as a comment
Reply "go ahead" → agent writes the code, opens a GitHub PR, posts the link back to Linear
Comment @ai-developer-agent on any PR → agent posts a structured code review or pushes fixes directly to the branch
No rigid commands. Natural language. The agent behaves like a real team member.
How It's Built
Claude Code CLI running in containers on AWS ECS Fargate
Lambda webhooks connecting Linear and GitHub events
Linear OAuth + GitHub App integration for secure, scoped access
Secrets managed properly, monitoring and error handling throughout
The Hard Parts I Solved
OAuth token refresh and GitHub App JWT authentication
Fargate image caching and Docker manifest issues
Model cost control and reliable repo-detection logic
Stack: Claude Code · AWS ECS Fargate · Lambda · Linear API · GitHub App · Docker · OAuth
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