I design scalable cloud architectures and build production-ready systems on AWS using microservices, containers, and infrastructure as code.
My path to cloud engineering wasn't a straight line. I started in a completely different field, then found myself drawn to the elegance of distributed systems — the idea that thousands of small, independent pieces could work together to do something neither could alone.
That cross-country move from India to the US — and an MS in Computer Science at the University of Michigan — sharpened how I think: not just about building systems, but about building systems that don't need you watching them.
Today I'm obsessed with the intersection of AI and cloud infrastructure — specifically, how to make intelligent systems reliable, scalable, and production-ready. If I do something twice, I've already written a script for it.
"If I had to do it twice, I've already scripted it."
— Engineering philosophy
How I work
Core stack
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Production-grade cloud architecture across microservices, AI, and infrastructure automation.
02 — AI Infrastructure
A serverless AI agent powered by AWS Bedrock and Lambda, with conversation state managed in DynamoDB. Designed for 99.99% availability using Route 53 failover and monitored end-to-end via CloudWatch — bridging foundation models with production cloud infrastructure.
03 — DevOps Automation
End-to-end CI/CD pipeline using AWS CodePipeline + CodeDeploy with blue-green deployment strategy. Infrastructure provisioned entirely via Terraform across EC2, Auto Scaling Groups, and ALB — multi-stage Docker builds improved build efficiency by 40%.
04 — Cloud Architecture
Globally distributed static platform on S3 + CloudFront benchmarking sub-100ms latency across multiple geographic regions. Serverless backend via API Gateway + Lambda + DynamoDB with Route 53 failover simulation achieving 99.99% availability design.
05 — Security Pattern
A cloud security architecture pattern implementing IAM least-privilege, network isolation via VPC + private subnets, and encrypted data pipelines. Built to demonstrate defense-in-depth principles for production workloads handling sensitive data.
Engineered high-availability AWS Linux workloads within hybrid cloud infrastructure, optimizing Kubernetes resource allocation to reduce compute costs by 22% while sustaining 99.95% uptime.
Automated deployments via Jenkins CI/CD pipelines and Git, reducing release cycle time by 45% across staging and production environments.
Implemented root-cause analysis using centralized ELK Stack logging, decreasing MTTR to under 30 minutes during high-severity incidents.
Enhanced distributed streaming via Apache Kafka on AWS-hosted clusters, reducing end-to-end event latency to under 200ms during peak transaction windows.
Provisioned development infrastructure using AWS EC2, S3, and IAM, enabling environment readiness within 2 hours for new project teams across multiple internal applications.
Containerized internal applications with Docker on Linux, eliminating environment drift and decreasing deployment-related defects by 18 tickets per quarter.
Implemented monitoring with Amazon CloudWatch and shell scripting, cutting manual health-check efforts by 6+ hours per week across shared development clusters.
Cloud Solutions Architect
Deployed microservices on ECS Fargate, automated CI/CD via CodePipeline + CloudFormation. 99.9% availability with Aurora PostgreSQL Multi-AZ.
AWS Cloud Solutions Architect
Architected globally distributed static platform on S3 + CloudFront. Serverless backend with Lambda + DynamoDB, sub-100ms latency globally.
Open to full-time roles, contract work, and interesting cloud infrastructure problems. If you're building something that needs to scale — I want to hear about it.
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