Profile

I build production-grade AI systems with a security-first mindset.

My background spans cyber investigations, mobile development, and full-stack engineering. That combination shaped how I approach modern AI:

• Assume adversaries exist
• Design for failure modes
• Build observable, controllable systems
• Ship products that real users depend on

Today I focus on applied LLM architecture, multi-model orchestration, and AI-powered platforms with real operational value.

Core Expertise

AI & LLM Engineering

• Multi-model orchestration and deliberation
• GPT-4 / Claude production integrations
• Vision + text multimodal pipelines
• Prompt evaluation and benchmarking
• Retrieval-augmented generation (RAG)
• Local LLM deployments (LM Studio)
• Async agent architectures
• Guardrails and structured output design

Backend & Systems Architecture

• Python (FastAPI, Flask)
• Async services and microservices
• REST APIs and WebSockets
• PostgreSQL and backend design
• Dockerized pipelines
• Observability and system resilience

Security & Research

• Threat modeling and adversarial thinking
• Applied cryptography and blockchain research
• Defensive architecture for AI systems

Frontend & Mobile

• TypeScript / React dashboards
• Real-time interfaces
• Native Android (Java, Kotlin)

Production Work & Open Source

🔹 LingoScreen

Founder / Engineer — AI Image Translation SaaS

A production SaaS platform that translates text inside images using vision models + LLM processing.

Scope:

• Production vision model pipelines
• Custom post-processing logic
• Scalable backend infrastructure
• Live deployment with real customer usage

🔹 PolyCouncil

Open-Source Multi-Model LLM Deliberation Engine

Runs local models in parallel, scores responses with a shared rubric, and produces a consensus answer — ideal for comparing ensembles and evaluating model behavior. PolyCouncil Repo

Stack: Python, Asyncio, LangChain, LM Studio

🔹 Shard

Distributed P2P AI Inference Network

A peer-to-peer system where browsers can contribute WebGPU compute as “Scouts”, and more powerful verifier nodes finalize model outputs. Designed to explore decentralized shared AI inference workloads. Shard Repo

Highlights:

• Browser-based compute contribution
• Mesh networking via libp2p
• Hybrid local/remote inference
• Experimental distributed AI platform

🔹 Koda

AI Browser Agent Framework

An extensible AI agent environment with support for multiple models (Gemini, OpenAI, Claude), browser automation, self-healing selector logic, computer vision heuristics, and scalable execution. Koda Repo

Features:

• Multi-LLM support
• DOM / UX automation intelligence
• Confidence / belief network designs
• Distributed execution components

🔹 SituationRoom

Real-Time Intelligence Dashboard

Aggregates live geopolitical and market data into a React dashboard designed for continuous situational awareness.

Stack: TypeScript, React, real-time APIs

🔹 Ethereum Address Collider

Applied Cryptography Research Tool

Explores theoretical aspects of Ethereum address generation and collision concepts.

🔹 DontPause

Android Media Utility App

Prevents notification interruptions during media playback — demonstrates practical native Android problem solving.

Philosophy

LLMs are powerful, but without structure they’re unpredictable.

My approach centers on:

• Turning probabilistic outputs into structured, testable systems
• Designing observable AI pipelines
• Building with reliability and failure resistance in mind

AI should be treated like infrastructure — not magic.

What I’m Looking For

• Senior AI / LLM Engineering
• Security + AI integration teams
• Founding engineer roles at ambitious AI startups
• Applied AI products with real users

If you're building systems where reliability, structure, and security matter — we’ll probably get along.

Contact

📧 pierce.trent@gmail.com
🐦 https://twitter.com/severesig
💻 https://github.com/TrentPierce

From physical security to digital systems to applied AI — the goal remains the same: build things that hold up under pressure.