Justin Karr

Machine Learning Engineer. I build AI agents that run in production.

20+ Years Intelligence & Data Operations · 11 AI Agents in Production · Autonomous Trading & Research Systems

Justin Karr

About

I spent five years in the Marine Corps and sixteen at NGA doing geospatial intelligence, through three combat deployments. For most of that I was the person using the tools, not building them — until I got tired of doing by hand what a script could do and started automating my own job. That turned into thousands of analyst hours handed back to the people who needed them.

I build AI now, at a different scale but for the same reason. Production systems with real users and real failure modes: a multi-agent platform running 30+ pipelines a day, evaluation harnesses, and the unglamorous plumbing that keeps the whole thing honest. Having depended on tools that looked impressive and fell over when it counted shapes how I build ones that don't.

I hold my own work to that standard. My independent trading research pre-registers every hypothesis and throws out the ones that don't survive honest assumptions — I've killed far more ideas than I've kept, and that's the point. A system that tells you what you want to hear is worse than no system at all.

I'm at my best when the problem is ambiguous, the stakes are real, and the thing actually has to ship.

What I build

The Gauntlet — Systematic Trading Research Stack

A multi-stage strategy-validation pipeline built to kill false edges before capital is ever risked. Research infrastructure spanning futures, forex, and options, all measured against a deliberately dumb baseline.

  • Pre-registered and tested 11+ trading hypotheses — and honestly rejected the majority, with the negative results published to a killed-hypothesis registry
  • Honest-fill and cost modeling (trade-through vs. touch fills, stop-first-on-same-bar, slippage, deflated Sharpe) that collapsed one candidate from PF 3.15 to PF 1.11 — correctly discarded
  • Staged validation: dedup → honest fills → placebo/parameter batteries → walk-forward out-of-sample → cross-instrument → tick-level fill validation → live forward paper testing
  • Engines across asset classes: a .NET 8 / NinjaTrader futures harness, a MetaTrader 5 prop-firm evaluation system, an SPX options IV/Greeks pipeline over 751 sessions, and a strategy × instrument × sizing-overlay lab
  • Published a reusable quant-research toolkit — 10 modular tools, 135 automated tests
PythonC# / NinjaScript.NET 8PandasNumPyWalk-Forward ValidationDeflated Sharpe

Nighthawk — Live Execution Bot

The one strategy that survived the Gauntlet, ported into a production NinjaTrader trading bot and currently in live forward testing on micro futures.

  • Bracket-order execution layer: partial fills, OCO stop/target management, trailing stops
  • Per-trade and daily risk limits with an automated safety-flatten kill switch
  • Prop-firm compliance engine enforcing trailing-drawdown and minimum-trading-day rules
  • Persistent trade tracker and live dashboard for forward-test measurement
C#NinjaTrader 8Order ManagementRisk ControlsLive Forward Testing

Job-Hunt Agent

An end-to-end automated job search: scrape, score, tailor, track. Built for myself, and it is how this application reached you.

  • Scrapes and ranks 800+ live postings against a weighted fit model (title, description, location, disqualifiers)
  • Auto-selects the right resume base per posting and generates a tailored .docx with a "Key Qualifications" block built only from skills I actually have that appear in the posting — ATS optimization without the lying
  • Drafts a matched cover note per role and tracks every application through a 9-stage pipeline
  • Company-discovery pass that surfaces smaller employers the big boards never show
  • Wrapped in one-click launchers so the whole loop runs without touching a terminal
Pythonpython-docxWeighted ScoringWeb ScrapingPipeline Automation

Storm Intelligence Pipeline

Real-time severe weather monitoring system that detects hail events and triggers automated property lead collection across 11 states.

  • < 5 minute response time to NOAA severe weather alerts
  • Automated forecast → property collection → outreach pipeline
  • GeoJSON parsing with multi-state coverage
PythonNOAA APIGeoJSONAsync Processing

LEDGER Enrichment Engine

7-stage async enrichment pipeline that transforms raw business filings into scored, qualified leads with full contact profiles.

  • 5,400+ filings processed, multi-source data fusion
  • Tiered classification (A/B/C) feeding outreach prioritization
  • Browser automation with anti-detection for protected data sources
PythonPlaywrightAsync/AwaitPostgreSQL

RAG Documentation System

Production-grade retrieval system for multi-version enterprise documentation using semantic search.

  • Handles evolving datasets — add new versions without retraining
  • Metadata-aware responses with cross-version comparisons
LangChainChromaDBOpenAI EmbeddingsVector Search

Skills

AI/ML & Agents

LLM IntegrationMulti-Agent OrchestrationPrompt EngineeringHybrid ScoringRAG ArchitectureEvaluation Frameworks

Languages

Python (Expert)SQLJavaScriptC# / NinjaScriptR

ML Stack

PyTorchscikit-learnPandasNumPyTensorFlowLangChain

Databases

PostgreSQL/PostGISSupabaseChromaDBFAISSNotion APIOracle

APIs & Integration

FastAPIWebhook ArchitectureOAuth12+ Production Integrations

Infrastructure

Linux AdministrationCron OrchestrationGitCI/CDDocker

Quant & Trading Systems

Honest-Fill BacktestingWalk-Forward ValidationDeflated SharpeRisk & Position SizingOrder Management / ExecutionMarket Microstructure

Experience

2025 – Present
Founder & Lead ML Engineer ATLAS AI Platform
2024 – 2025
Senior Data Scientist ECS Federal (NGA)
2022 – 2023
Senior Data Scientist Integrity First Technologies (NGA)
2019 – 2021
Business Analyst / GIS Specialist SAIC (NGA)
2004 – 2018
Intelligence Analyst & GEOINT Specialist NGA / USMC

Let's talk.

I'm open to full-time ML engineering roles, contract work, and interesting problems.