About

I turn operationally messy problems into production systems

Most of my career has happened at the boundary between complex technology and real-world operations: robots that scan retail shelves, booking systems inside an airline's loyalty platform, a single codebase shipping fifty branded apps, and — today — AI systems that answer a service business's phones at 2 a.m. and put real jobs on real schedules.

The pattern across all of it is the same. Somebody has a workflow that matters commercially and is held together by tribal knowledge, manual coordination, and a few overworked people. My job is to understand that operation well enough to encode it — then design, build, integrate, deploy, and keep improving the system that runs it. I work across the whole path: customer discovery, architecture, implementation, third-party integrations, production deployment, and the unglamorous post-launch review that makes systems actually good.

I co-founded Playmaker, where I lead the technical side of a conversational AI platform for home-service businesses. I measure that work the way the businesses do: appointments booked, close rates, revenue attributable to the system — not demo quality.

Experience

Playmaker IO

Co-Founder & Technical Lead

January 2026 – Present · Austin, TX

Conversational AI platform for home-service businesses. I own the technical side end to end: customer discovery with CSRs and dispatchers, solution architecture, implementation, production deployment on AWS, and post-launch optimization. The systems — autonomous voice agents and retrieval-grounded copilots integrated with ServiceTitan and FieldRoutes — book roughly $20K/month in appointment revenue and lifted a client's inbound close rate from 72% to 92%. The AWS CI/CD pipeline I built with GitHub Actions cut new-tenant deployment time by 80%.

Voice-agent case study →RAG copilot case study →

WOLF

Lead Mobile Developer

May 2025 – January 2026 · Austin, TX

A Shopify-like staffing platform: one codebase powering 50+ white-labeled workforce apps across healthcare, hospitality, and industrial verticals. I designed the configurable multi-tenant React Native and Node.js architecture and built the Fastlane + GitHub Actions pipeline behind 1,000+ production releases — lifting release velocity by 35%, customer retention by 20%, and app-store ratings by 25%.

Platform case study →

Snowfall Travel · Emirates Skywards

Senior Full-Stack Developer

September 2023 – February 2025

Enterprise travel integrations inside the loyalty platform of Dubai's largest airline: Okta SSO identity, multi-provider rail search and booking APIs supporting millions of monthly searches, distributed booking orchestration, and hybrid card + loyalty-miles payments — shipped under enterprise review processes and a hard deadline.

Integration case study →

Bossa Nova Robotics

Lead Mobile Developer

September 2022 – June 2023

Retail shelf-scanning robotics. I built the field data-capture and labeling tools used by non-technical field teams across hundreds of store locations — the operational front end of the company's computer-vision training pipeline, designed for bad lighting, spotty connectivity, and users who don't read manuals.

Field tooling case study →

Hydra Technologies

Full Stack Developer

May 2019 – August 2022

Supply-chain optimization platform. Implemented enterprise operational-planning applications for a $500M client, improving operational efficiency by 35%, and built a cross-platform React Native app for enterprise suppliers backed by Python ML services on AWS Lambda — improving inventory coordination and forecasting accuracy by 30%.

B.S.&A., Mechanical Engineering — University of San Diego, 2020

How I Work

01

Sit with the operation before touching the system

The requirements that matter — urgency rules, pricing boundaries, who actually decides what — live in people's heads, not in tickets. Discovery is engineering, not a phase before it.

02

Encode business rules where they can be enforced

Configuration and code, not prompts and hope. If a rule matters commercially, its enforcement should be testable.

03

Every system ends in an explicit, recorded state

Booked, escalated, failed-with-context — never "it just ended." This one invariant is what makes production systems measurable, debuggable, and trustworthy.

04

Judge work by the operational number

Close rate, booked revenue, deployment time, calls answered. Technical elegance that doesn't move an operational number is a hobby.

05

Autonomy inside boundaries

For AI agents and for engineers: conservative authority at launch, widened as production behavior earns it. Trust is built from what a system declines to do alone.

What I'm available for

Senior roles

Forward Deployed Engineering, Applied AI, AI Solutions Engineering, senior product or platform engineering, technical leadership, and founding technical roles.

Selected engagements

Scoped consulting and contracting: production AI and voice AI systems, CRM and platform integrations, technical audits and implementation plans, and short-term technical leadership.

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