////Cloud Engineering
Infrastructure as code, reliability engineering, and cost control — the operating side of cloud work, not just the provisioning side.
Cloud engineering on AWS is less about picking services and more about the decisions that only surface after launch: where state lives, how failures propagate, and why the bill grows while traffic stays flat. I provision with AWS CDK and Serverless Framework rather than console clicks, instrument what I ship so the next incident is diagnosable in minutes, and treat cost as a metric that is measured continuously instead of reviewed once a quarter.
If a Lambda function, queue, or database is configured by hand in the AWS Console, it cannot be reproduced, reviewed, or rolled back. Every environment I run is defined in version-controlled infrastructure-as-code, so a new region, stage, or teardown is a pull request rather than an afternoon of clicking. Secrets live in Systems Manager Parameter Store or Secrets Manager — never in deployed .env files.
Reliability on AWS is mostly a decoupling problem. Ingestion is kept separate from processing with SQS so that a slow or unhealthy downstream cannot take the API down with it. Functions get explicit timeouts and reserved concurrency, handlers are written to be idempotent because at-least-once delivery is the reality of every managed queue, and dead-letter queues mean a failed message is recoverable rather than lost.
Cloud cost work is measurement first. Resource-level usage is aggregated per service, idle and oversized resources are identified, and a health index scores each candidate before anything is touched. On SubTrackHub this pipeline used LLMs to generate safe cleanup recommendations with a rollback path, and the result was an average AWS spend reduction of 35% without a production incident.
How I built a pipeline that aggregates AWS resource usage metrics, computes cloud health indices, and leverages LLMs to propose safe cleanup recommendations without breaking production.
Cold starts, connection pooling, IAM lockdowns, and event-driven architectures. Here is a battle-tested guide to scaling serverless applications.
Moving from a VPS to AWS is not just a hosting change — it is an architecture decision. Here is a practical guide to migrating startups to AWS, with real cost comparisons, serverless patterns, and lessons from production.
A showcase of my recent projects, focusing on scalable architecture, AI integration, and sleek user experiences.
A real-time monitoring platform for websites and servers featuring uptime tracking, DNS monitoring, SSL expiry alerts, and AI-driven insights for anomaly detection. Monitors 500+ endpoints with 99.9% check success and sub-second alert latency.
Enterprise-grade AI observability platform with a Zero-Trust/GDPR-compliant architecture. Features local in-memory PII scrubbing and direct proxy bypass. Processes 2M+ traces/month, reducing debugging cycles by 70%.
A developer environment Secret Manager that solves the problem of secret sprawl. Secures secrets locally with zero-overhead AES-256-GCM. Active in production across 5+ engineering teams.
Analyzes cloud infrastructure usage to identify idle resources. Integrates LLMs to generate efficiency scores and recommendations, yielding an average AWS spend reduction of 35%.
Converts YouTube videos into short, captioned clips optimized for social media using AI-based transcription and automated highlight extraction.
A responsive NFT showcase website featuring curated collections. Hosted on AWS S3 with Route 53 domain routing for scalable delivery.