> initializing profile...

ZACH FIRESTONE

SOFTWARE ENGINEER

Senior backend engineer based in Orlando. I build reliable, scalable systems — and I care about the code that runs them. Python, Go, Kubernetes, and whatever gets the job done.

GitHub @zachfire9 LinkedIn /in/zachfirestone Goodreads @zachfire9
Zach Firestone
ORLANDO, FL

01 // ABOUT

I'm a backend engineer and infrastructure specialist. I build services that scale, write pattern-driven code backed by solid tests, and own the full lifecycle — planning through deployment and support.

I've been keeping up with the AI side of things — building services that integrate with LLMs, experimenting with agentic workflows, and staying current on how AI tooling is changing the way we write and ship code. It's moving fast and I find it genuinely interesting.

02 // TECH STACK

Languages

Python Go PHP JavaScript Node.js SQL

Infrastructure

Kubernetes Docker AWS CI/CD Terraform

Data & Messaging

MySQL PostgreSQL MongoDB RabbitMQ Redis

Practices

Design Patterns Testing Agile Code Review API Design

03 // CURRENT ROLE

Senior Software Engineer II

Capacity // Feb 2023 – Present

My current role is focused primarily on developing AI and agent-based applications, working mostly in Python and Go. I work on a fully remote, distributed engineering team and collaborate closely with product to take new features from requirements through implementation, while still contributing to some of the more traditional backend services our team supports.

A lot of my recent work has been around our internal agent platform, which uses LLMs, RAG, and MCP tooling. AI has also become a regular part of my development workflow — I use AI-assisted development extensively for coding, technical planning, code reviews, and debugging.

Go + LLM platform

Voice-call analysis service

Built a Go service that uses our internal LLM platform to analyze voice-call transcripts, perform sentiment analysis, and classify how users responded to calls.

AI safety

Configurable guardrails

Led the implementation of configurable AI guardrails for our agent platform, integrating with Azure's native content-safety functionality and allowing customers to tune filtering for their use case.

Agent platform controls

Custom content filtering

Designed and implemented a configurable filtering system that allows customers to define their own content rules and control how matching conversations are handled.

Flask GraphQL RabbitMQ Elasticsearch AWS Kubernetes Datadog