These projects represent real engineering work across infrastructure, automation, platform reliability, and product development. Each case study covers the context, approach, implementation, and outcomes.

Featured Projects

1. Crypto Trading Bot

Summary: Python-based algorithmic trading engine for breakout, pullback, scalping, and volume spike strategies.

Context / Problem: Manual execution of multi-strategy trading across volatile crypto markets was inconsistent and couldn’t run 24/7 without constant monitoring.

What I Built / Handled:

• Migrated from decentralized AWS Lambda to a monolithic, systemd-managed service to reduce execution latency and cloud costs
• Built position sizing, stop-loss logic, and AI-driven risk assessment (caution → panic → recovery modes) based on real-time market data and sentiment analysis
• Automated deployment and configuration via AWS SSM Parameter Store

Outcome: Reduced manual intervention, improved execution consistency during high volatility, and automated risk management with AI support.

Tech Stack: Python, AI/ML models, SQLite, APScheduler, AWS SSM, Nginx, systemd, Linux

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2. smallPict https://smallpict.tuxnoob.com

Summary: WordPress plugin for automated image compression and WebP/AVIF conversion with serverless backend.

Context / Problem: WordPress sites suffer from performance bottlenecks during bulk image processing, especially with heavy uploads.

What I Built / Handled:

• Decoupled image processing to AWS Lambda backend to offload CPU-intensive work from main servers (~80% AI-generated optimization logic)
• Built WordPress integration for quota management, API routing, and failure recovery
• Created dual CI/CD pipelines for WordPress.org distribution compliance

Outcome: Faster media handling, reduced server load, and production-ready plugin distribution powered by AI image processing.

Tech Stack: PHP, WordPress Plugin API, AWS Lambda, Python, AI image models, S3, GitHub Actions

3. Broom.id Platform Modernization

Summary: Led infrastructure redesign, AWS ECS platform setup, infrastructure-as-code adoption, and cost optimization across development, staging, and production environments.

Context / Problem: When I joined, the AWS environment was still largely built around the default VPC, with many resources publicly exposed and very limited network isolation. There was no strong infrastructure-as-code foundation in place, and the platform needed a safer, more structured, and more maintainable operating model to support future growth.

What I Built / Handled:

• Redesigned the network layout into three dedicated VPCs for dev/staging, infra, and production
• Established controlled connectivity between environments so infrastructure services could reach all environments while production remained isolated from non-production traffic
• Introduced VPN-based administrative access and tightened security groups so SSH and PostgreSQL access could be restricted through controlled access paths instead of being publicly reachable
• Brought in Terraform as the primary infrastructure-as-code layer to standardize provisioning, environment setup, and ECS service deployment
• Introduced Ansible to support repeatable operational automation, configuration updates, lightweight migration workflows, and database upgrade tasks
• Evaluated AWS EKS versus AWS ECS for a new microservices-based project (Gearbox) and selected ECS as the more practical choice based on current service scale, delivery speed, and operational simplicity
• Built the platform foundation around Docker, AWS ECS, AWS ECR, Terraform, and GitHub Actions for automated build and deployment workflows
• Designed CI/CD pipelines that automatically built and deployed services based on branch activity, pushed container images to ECR, and sent Discord notifications on build failures
• Used Terraform to simplify ECS delivery by managing both task definitions and service updates through a more consistent deployment path
• Led migration of PostgreSQL and legacy application workloads into the redesigned VPC structure using pragmatic Ansible-assisted migration workflows
• Drove cloud cost optimization by identifying major cost drivers across NAT Gateway, AWS Fargate, EC2, and RDS, then applying improvements including Savings Plans, NAT replacement with ARM-based EC2 instances, ECS migration from Fargate to EC2 Auto Scaling Groups, Graviton adoption, and scheduled shutdowns for non-production resources
• Extended the engineering workflow with unit tests and SonarQube-based quality checks, and introduced RabbitMQ to support payment-related service requirements
• Solved ECS Spot interruption challenges in production with a hybrid On-Demand and Spot model to balance reliability and cost efficiency
• Built containerized self-hosted GitHub runners on AWS ECS to reduce dependency on standalone EC2-based runners
• Replaced Datadog in development and staging with a self-managed Grafana stack using Prometheus, Cortex, Loki, Promtail, Alloy, Tempo, and OpenTelemetry
• Managed PostgreSQL version upgrades ahead of AWS RDS end-of-life pricing changes using Ansible-driven automation

Outcome:

• Reduced monthly AWS spending from around $12,000 to $6,000, and later to around $5,000, through infrastructure and runtime optimization
• Improved security posture by moving away from broadly public exposure toward isolated networks and controlled administrative access
• Established a more maintainable and repeatable platform through Terraform-based provisioning and Ansible-supported automation
• Improved deployment consistency, engineering workflow quality, and operational visibility across multiple environments
• Created a stronger foundation for ongoing modernization, including production database migration, broader Graviton adoption, and legacy workload migration into the redesigned platform

Tech Stack: AWS VPC, ALB, NLB, EC2, ECS, Fargate, ECR, RDS PostgreSQL, Terraform, Ansible, GitHub Actions, Docker, RabbitMQ, SonarQube, Prometheus, Grafana, Loki, Tempo, Alloy, OpenTelemetry, Linux

Additional Work

Beyond the featured projects above, I’ve also worked on a wider range of infrastructure and platform initiatives across full-time, freelance, and contract roles.

Some of those projects are still being documented, and I plan to add them here over time as I continue organizing the technical details, decisions, and lessons learned from each engagement.

Other Platform Work

Multi-cloud infrastructure, Kubernetes deployments, Terraform IaC, observability stacks, and security hardening across AWS EKS, GKE, Alibaba Cloud, and enterprise environments.