The platform that designs and simulates a lunar lander, plus the AI agents that answer questions straight from its source of truth.
The hard part
High-volume analysis and simulation jobs that depend on each other, over data classified as CUI — so every row needs access control and every change needs a permanent history, without that bookkeeping becoming the bottleneck.
The decision
An event-driven architecture on Kubernetes with infrastructure defined in Terraform, so simulation work fans out instead of queueing behind itself. End-to-end simulation time fell by 40%.
Python
TypeScript
React
Terraform
Kubernetes
AWS
DynamoDB
PostgreSQL
SQS/SNS
Datadog
MCP
40% faster simulations · 5 engineers led · 2 production agents at 100% factual accuracy
Lead engineer, five-person team, 2024–present
AmigoWeGo
Status: RELEASED
Group trip planning that survives contact with a group chat.
The hard part
Several people edit one shared plan from a phone and a browser at the same time. Four questions have to stay answerable and consistent across both clients: what is the plan, who is where, what was decided, and who owes what.
The decision
Contract first. A single OpenAPI document generates both clients, so the Rust backend and the SwiftUI app cannot drift apart — the compiler catches a mismatch that would otherwise surface as a bug on somebody's holiday.
When a group uses this app, they should always know:
What's the plan? - Today's activities and what's coming next
Who's where? - Where everyone is during the trip
What's decided? - Clear record of group decisions
Who owes what? - Expenses tracked and settled without awkward chasing
Product overview
Home Claw
Status: RUNNING
A family assistant that answers from any room and never leaves the house.
The hard part
A useful home assistant needs the family's schedules, preferences and medical information. Every product that does this well sends that data to someone else's servers.
The decision
Privacy as an architectural constraint rather than a feature. The Mac Mini M4 Pro is the only machine that runs inference, and it and both Raspberry Pi voice stations are running; the Jetson arm bridge is planned, not built. No cloud dependency, no subscription, nothing crossing the LAN boundary — which rules out the easy answer and makes model size a hardware problem.
MLX
FastAPI
pgvector
Raspberry Pi
Jetson Orin Nano
Scroll the diagram sideways →
The LAN boundary is the design: every component that touches family data is meant to sit inside it.
Architecture — dashed boxes are planned
The Observer
Status: TRIALS
A camera that keeps only the good photographs of the dogs.
The hard part
An all-day camera produces thousands of frames and almost no keepers. Detecting a dog is the easy half; deciding which frames are worth keeping is the actual product, and it is a judgement call.
The decision
A phased proof of concept where each phase has to pass a gate before the next one earns any time: frame in, dog detection, keeper scoring, portrait crop out. The ladder stops early if a rung fails, instead of arriving at a finished pipeline that produces nothing worth looking at.
YOLO
OpenCV
Pi Camera 3
Python
Cards The Observer has made from real keepers — Pokémon-style for Luna, baseball-style for Max.
This repo is the proof-of-concept ladder; each phase has a pass/fail gate before the next one earns any time.
Project plan
Quadruped
Status: PHASE 0
Four legs from nothing: hardware, control software, CAD, simulation, and eventually reinforcement learning.
The hard part
A legged robot fails where mechanical design, power delivery and control meet, and each of those can quietly destroy the others — a servo that browns out the controller looks exactly like a bug in the gait.
The decision
Classic control first and reinforcement learning later, across eight phases that each end in an observable exit test. The power maths and the battery safety rules were written down before anything was energised.
Fusion 360
Jetson
MuJoCo
Raspberry Pi
PCA9685
…the robot stands in a neutral pose holding its own weight for several minutes without servo overheating or brownout; full-system current stays within BEC/battery limits.
Project plan, Phase 3 exit test
The Sentry
Status: SEASONAL
An animatronic that tracks people up the driveway every October.
The hard part
Computer vision is soft real-time and servo control is hard real-time. Run both on one processor and the vision work steals the timing the servos need, so the head moves in visible jerks.
The decision
Split the two across processors. A Raspberry Pi is the brain and does the image processing; a Pico or Nano is the muscle and does nothing but precise servo and LED timing. The interface between them is deliberately narrow: the Pi sends simple commands over a serial link, and the Pico executes them.
OpenCV
Raspberry Pi
Pi Pico
Arduino Nano
Adafruit PWM driver
Scroll the diagram sideways →
A Raspberry Pi will act as the "brain," handling all the complex image processing for object tracking. An Arduino Nano or Raspberry Pi Pico will act as the "muscle," receiving simple commands from the Pi and executing the precise, real-time control of the servos and LEDs.
Project plan, §1
How I work
Phases with exit tests
Every build gets a plan where each phase ends in a concrete, observable result. Nothing advances on the strength of feeling nearly done.
The maths before the power
Power budgets and safety rules get written down before a battery is connected, not after something releases smoke.
Honest status
Unfinished work is labelled unfinished, in the repository and on this page. A project that says Phase 0 is worth more than five that claim to be done.
Experience
Blue Origin
Lunar Data, AI & Applications
2024 – PresentNow
Lead Engineer
Serve as the Lead Engineer for a 5-person team, owning the full technical vision, architecture, and deployment strategy for a platform automating vehicle design, simulation, and performance tracking across all lunar program subsystems.
Built a handful of AI agents and put two into production, fully trusted by the team: MCP-connected agents that answer questions on technical performance measures and design parameters directly from the program's source of truth, an application this team also owns and develops.
Held the agents to 100% factual accuracy, the standard for lunar program data, with automated tests that check agent answers against known facts from the source of truth.
Built the APIs that the MCP servers call, giving agents structured, tool-based access to the source of truth instead of relying on model memory.
Initiated and led the adoption of agentic workflows and a shared repository context across the team, improving programming efficiency and decreasing human effort in debugging and fixing simulation and analysis job failures.
Designed and implemented a robust CI/CD and automation pipeline (using Python, Terraform, Kubernetes, and Datadog) to manage high-volume, interdependent analysis and simulation jobs, directly accelerating engineering and mission planning workflows, reducing end-to-end simulation time by 40%.
Architected the application for scale and compliance, utilizing React, TypeScript, Python, AWS DynamoDB/RDS (PostgreSQL), and AWS SQS/SNS. Secured CUI data and Configuration Management by implementing row-based access and full historical change tracking.
Collaborated across Systems Engineering, Program Leadership, and Mission Planning teams to gather requirements, manage product prioritization, and deliver full-stack solutions.
Developed a new lifecycle management platform to help systems engineers track key vehicle metrics; led the transition to a unified, data-driven solution, reducing engineering workflow inefficiencies.
Led the cloud deployment strategy, implementing Infrastructure as Code (IaC) with Terraform and deploying the system on Kubernetes, ensuring scalability and maintainability.
Developed interactive React visualizations and a custom data table, optimizing engineers' ability to analyze high-dimensional simulation data and accelerating the identification of critical performance regressions.
Promoted from Software Engineer III to Lead Engineer on the same program.
Agentic AI
MCP
LLM Evaluation
Context Engineering
GenAI
Python
TypeScript
React
Terraform
Kubernetes
AWS
DynamoDB
PostgreSQL
SQS/SNS
Datadog
Data Visualization
Blue Origin
Enterprise Technology
2022 – 2024
Software Engineer II/III
Managed and maintained critical platform services (user-service, file-service) written in Java and Python, ensuring 99.999% availability and reliability for space vehicle manufacturing.
Designed and implemented an event-driven system using AWS SQS/SNS and OpenSearch, enhancing engineers' ability to locate critical manufacturing data in near real-time.
Automated cloud infrastructure management using Terraform and Kubernetes, and integrated Datadog monitoring, improving visibility and responsiveness.
Enforced role-based access control (RBAC) for sensitive manufacturing data via REST and GraphQL APIs.
Java
Python
AWS
SQS/SNS
OpenSearch
Terraform
Kubernetes
Datadog
GraphQL
REST
Alteryx
Data Science R&D
2019 – 2022
Sr. Full Stack Software Engineer
Architected and deployed ETL orchestration pipelines (Apache Airflow/Prefect) to manage terabytes of data flows for model training and serving, supporting multiple internal ML applications.
Engineered scalable backend services using Rust and Python for data-intensive applications, including the development of full-stack applications with interactive D3.js/SVG visualizations.
Rust
Python
Apache Airflow
Prefect
D3.js
SVG
ETL
ML Pipelines
Education
MS Computer Science — University of Colorado (In Progress)
Full Stack Immersive — Galvanize
BS Kinesiology/Physiology — University of Louisville
