Dublin-based Applied AI Architect and GenAI systems leader focused on enterprise GenAI, LLM evaluation, AI governance, RAG, agents, and regulated AI deployment. Currently AI Decision Science Manager at Accenture Ireland.
I build production GenAI systems for regulated environments: RAG, agents, LLM evaluation, observability, security controls, data architecture, and AI governance.
| Pillar | Evidence | What it shows |
|---|---|---|
| AI governance foundations | NSF case study | Greenfield AI governance, technical review, security exercises, community, and data architecture inside a CAIO function |
| Production optimization | 99.6% ML cost reduction | Hands-on production ML engineering, scale, observability, and cost discipline |
| Model readiness | LLM Evaluation Workbench | Public eval harness for reliability, governance behavior, groundedness, cost, latency, and reviewable artifacts |
| Evaluation research | PAEF preprint | Atomic contract-compliance evaluation across 193 contracts and 7,913 labeled policy checks |
| AI-assisted delivery | Agentic AI SDLC Platform | Governed prototyping with Azure DevOps as system of record and Claude Code-integrated execution |
| Package | Distribution | Purpose |
|---|---|---|
arxiv-embedding-benchmark |
PyPI, CLI | Reproducible embedding and retrieval evaluation for scientific RAG workflows |
eldritch-thinking |
npm, npx |
AI-interface status messages for CLIs, dashboards, and agent UIs |
| Signal | Evidence |
|---|---|
| AI / ML leadership | AI Decision Science Manager; former ML Engineering Manager and Lead Scientist roles |
| Agentic AI | FastMCP, multi-agent orchestration, tool routing, workflow observability, agent UI patterns |
| RAG / retrieval | Embedding evaluation, scientific retrieval, academic paper similarity, knowledge workflows |
| MLOps / AI infrastructure | Dockerized local ML workbench, model serving, experiment tracking, deployment patterns |
| Geospatial ML | Satellite imagery, remote sensing, change detection, IARPA SMART evaluation pipelines |
| Scientific ML | Postdoc research code, hyperspectral plant phenotyping, computational biology background |
| AI strategy | Former strategic AI advisor experience at NSF and enterprise AI delivery experience |
The public technical repos reinforce one through-line:
governed AI adoption -> measurable model readiness -> production AI delivery
| Priority | Featured work | What it shows |
|---|---|---|
| 1 | llm-eval-workbench |
Model-readiness evaluation with configs, datasets, adapters, CI, failure taxonomy, cost, and latency |
| 2 | arxiv-embedding-benchmark |
Published PyPI package for embedding comparison, retrieval evaluation, and scientific RAG |
| 3 | mcp-orchestrator-workbench |
React + FastAPI + FastMCP workbench for agent and workflow orchestration |
| 4 | local-ml-workbench |
Self-hosted MLOps lab for model serving, experimentation, evaluation, and RAG |
| 5 | claude-burn-check |
Claude Code context diagnostics and practical developer-tool packaging |
| 6 | demeter |
Historical scientific ML roots in hyperspectral plant phenotyping and sensor optimization |
| Theme | Keywords / tools |
|---|---|
| Agentic systems | FastMCP, MCP, multi-agent orchestration, tool use, workflow execution, agent observability |
| Retrieval systems | RAG, embeddings, vector search, scientific retrieval, academic paper similarity, model evaluation |
| MLOps | Docker, local GPU workbenches, model serving, experiment tracking, dataset labeling, CI smoke checks |
| Geospatial AI | Satellite imagery, remote sensing, change detection, segmentation, object detection, evaluation pipelines |
| Applied ML | Vision transformers, contrastive learning, Siamese networks, UNet/ResNet, scientific workflows |
| AI infrastructure | Cloudflare access, containerized services, FastAPI, React, Azure Container Apps, observability |
| Human-facing AI | Interfaces, diagnostics, design systems, explainability, inspection, replay, and workflow visibility |
local-ml-workbench is the local AI lab: a Dockerized environment for datasets, annotations, training, evaluation, model tracking, local LLM serving, and research notes.
arxiv-embedding-benchmark is published on PyPI and compares embedding models on academic paper similarity tasks so model choice is based on retrieval behavior rather than vibes.
llm-eval-workbench packages a regulated-enterprise readiness workflow with datasets, configs, adapters, governance and groundedness checks, cost and latency tracking, explicit failure categories, and reviewable run artifacts.
mcp-orchestrator-workbench explores how agent workflows should be planned, executed, logged, and replayed across tool servers and UI surfaces.
demeter connects the current AI systems work back to postdoc research in TerraRef hyperspectral plant phenotyping and sensor/filter optimization.
eldritch-thinking is published on npm as a tiny npx-runnable AI-interface utility; design-system carries the broader interaction and visual language for clearer AI workflows.
ODNI/NGA postdoc -> Booz Allen Hamilton Lead Scientist -> Accenture Federal Services ML Engineering Manager -> NSF Lead Data Scientist GS-15 -> Accenture Ireland
Selected highlights:
- Technical lead experience on IARPA SMART satellite ML evaluation pipelines.
- Former strategic AI advisor work at NSF.
- Experience advising, building, and evaluating applied AI systems across research, government, and enterprise contexts.
- PhD in Biology with computational focus from NMSU.
- NSF Graduate Research Fellow, 2015-2018.
- Claude Certified Architect, Early Adopter, 2026.
| Year | Venue | Topic |
|---|---|---|
| 2024 | IEEE IGARSS | Satellite ML / remote sensing |
| 2023 | IEEE IGARSS | Geospatial change detection |
| 2023 | WACV | Computer vision |
| 2020 | Cell Chemical Biology | Mosquito microbiome |
| 2018 | Annals of Behavioral Medicine | Epidemiology forecasting |
| 2018 | arXiv | Agent-based traffic modeling |
- CV + portfolio: codychampion.bitsandbeakers.com
- LinkedIn: linkedin.com/in/cody-champion