About

Learn more about me and this project.

About Me About This Repo

Who I Am

Experienced SaaS/PaaS engineer with a passion for building custom, scalable systems solving complex problems and delivering high-impact solutions that drive business value. Over my career, as I developed tiered, role-based applications, I continually came across having to build hierarchies in applications - and I found that it was a common problem that had no good solutions. This portfolio portal implemented using AI augmented workflow plans is my playground to work with AI on an entire SaaS lifecycle. As I continue to learn the role of AI in software development, I am excited to explore how it can be used to build even more efficient and scalable systems.

Background

I've spent decades building production systems focused on enterprise SaaS and content management solutions from workflows to real-time compliance pipelines. Among the numerous custom solutions in various verticals are fundraiser management (non-profit), artwork management (custom), talent management (custom), productization of third party integrations into existing solutions (recommendations, reviews), end-to-end PaaS deployment using AWS step-functions and SES with S3 trigger from a web form, AI augmented deployment workflow (this repo). At this point in my career, I'm most comfortable working with AI Augmented workflow in any programming language at and organization level for a small to mid-size company where my efforts include exposure to AI Planning and execution pipeline that involves distributed systems, type-safe APIs, and infrastructure-as-code.

Core Skills

Rust · TypeScript · Python · AWS (Lambda, EFS, S3, Cognito, CloudFormation, IAM, SES, RDS) · SQLite · OpenTofu · Axum · Tokio · Askama · OpenAPI · SaaS · PaaS · SDLC

Engineering Philosophy

Plan deliberately, but assume plans will evolve. Validate dependencies early, and continuously refine through execution. Prefer boring infrastructure, explicit systems, and strong type guarantees over hidden complexity. Optimize for clarity, debuggability, and long-term maintainability. Treat AI as a collaborator, not an oracle—design systems that verify, constrain, and observe its outputs. Build feedback loops into every layer: from compile-time guarantees to runtime observability to user outcomes. Every abstraction should earn its place—and justify its cost over time.