From patented neural-network work in regulated finance to current agent, eval, model-routing, and control-plane research, my focus is the same: capability with custody.
I'm an AI engineer and patent holder with an extraordinary background spanning large-scale production AI, high-stakes emergency response, and frontier AI infrastructure.
One career, three rooms. I am a named inventor on US11176557B2, a behavioral-transaction model for suspicious-activity detection and SAR workflows built in an institution-scale environment serving 100M+ customers[1]. I delivered a baby on scene[2]. I am now focused on trustworthy AI infrastructure: agents, evals, model-routing control planes, and transaction-intelligence research at trillion-record scale.
The sector is not the boundary; it is the proving ground. The through-line is AI behavior under consequence: opacity, false confidence, escalation, auditability, and control.
Guardrails, holdout checks, audit trails, escalation paths, and interpretability-adjacent workflows that keep model behavior legible.
Tool‑using agents, coding workflows, and long‑horizon automation designed to leave reviewable evidence.
Training, serving, eval, and deployment discipline behind ambitious AI goals — the infrastructure that keeps models honest.
Systems that hold up where wrong answers have operational, financial, or human consequence.
A patented behavioral-transaction model for suspicious-activity detection and SAR workflows.
A convolutional neural network for suspicious‑activity detection on transaction data, developed in a top-tier global bank environment serving 100M+ customers and governed through regulated change-control expectations.
AI systems for interpreting labor, skill, and organizational signals.
Principal‑engineer work on AI across a global enterprise's human‑capital function — turning public labor data and internal signals into a calibrated forward view used for workforce and contractor strategy.
Agents, model gateways, eval loops, and workflows that improve software and ML systems under review.
Frontier coding agents, model‑routing control planes, research agents, and evaluation loops connected to review, custody, and change‑control rails — the same platform pattern needed when AI becomes shared engineering infrastructure.
Agents propose hypotheses, but the platform owns the rails.
Domain-specific research agents, retrieval agents, and coding agents turn questions into candidate experiments, implementation plans, and review artifacts. The goal is not unchecked autonomy; it is a system where every suggested change has context, scope, and an escalation path before it becomes execution.
A GPU-backed service for turning ideas into repeatable model runs.
The execution layer accepts structured job specs, validates them, generates experiment programs, runs training in isolated workspaces, and records code, metrics, artifacts, and logs. It connects frontier-agent reasoning to real ML execution without letting the agent become the source of truth.
Every run has to leave behind evidence.
Experiments are compared through validation and holdout metrics, calibration summaries, saved model artifacts, result journals, and failure notes. The pattern is deliberately reviewable: generated code, data splits, scores, and decisions stay attached to the run.
A platform problem, not a model-call problem.
The hard part is giving engineers powerful frontier-model capabilities while preserving secure access, observability, evaluation, lifecycle discipline, and escalation boundaries.
Different surfaces, same control problem.
Whether the surface is a suspicious‑activity model, an internal developer platform, a research agent, or a frontier‑model evaluation loop, the system problem is the same: increase capability without losing control.
My work sits at that boundary — model behavior, engineering velocity, evaluation, auditability, and human escalation.
AI systems for environments where mistakes have operational consequence.
Evals, controls, and review loops for agentic systems and model‑assisted work.
Shared infrastructure, model routing, logging, custody, and rollout discipline.
Jeremiah is a strong technologist, especially in the AI/ML field, and a leader. He has a lot of expertise in AI and ML techniques and is an expert in the field. As a technology leader, Jeremiah directs the work of his team, guiding them toward the techniques that should be pursued, coaching them, and ensuring the success of the project.
He has successfully established relationships with partner teams, and Jeremiah is clear in his communication, does not hesitate to speak up, and is independent. There are a number of initiatives upcoming, and Jeremiah will be key in a lot of those efforts.
Jeremiah excels in levelheaded leadership when it matters most. When precise decisions are required to maintain operational understanding while the world is burning down around him, Jeremiah has the ability to focus through the noise and see the problem for what it is.
He treats simulations and training as if they are the real thing, because he takes his role extremely seriously — as the first, last, and only line of defense between the circumstances of the worst day of someone's life and the outcome.