C. Shawn Brinkley

Builder. Leader. Bridge.

I build AI systems, lead the teams that deliver them, and translate between the people who build the technology and the people who need to trust it. More than 25 years across FinTech, applied ML/AI strategy, data architecture, and enterprise delivery in regulated environments.

The Intersection of Three Things Most People Only Do One Of

Technical architecture, organizational delivery, and stakeholder alignment in regulated environments. I have led engineering organizations, stood up operating models, navigated model risk governance, and shipped production ML tools at a Fortune 100 bank. I have also sat across from CIOs and risk officers and explained why the work matters in language that doesn't require a computer science degree to follow.

I build AI systems with architectural discipline, evaluation rigor, and awareness of what regulated environments demand. My development approach is AI-assisted. I drive architecture and design decisions, AI accelerates the implementation, and I own the understanding. Every system I build includes evaluation from Day 1, not as an afterthought.

Most AI engineers are credible in the engineering room. Most business consultants are credible with business stakeholders. Most governance professionals understand policy. I operate across all three.

What I Do

AI Strategy and Advisory

Technology vision, AI/ML roadmaps, and strategic planning for executive leadership. Organizational operating models for AI adoption and advancement in regulated industries.

Data Architecture and Integration

Data and application integration audits, data flow mapping, and architectural assessments with current-state findings and future-state recommendations.

Enterprise Delivery Leadership

PMO scaffolding, program strategy, portfolio management, and metrics-driven tracking for technology organizations. From discovery through delivery.

Agentic AI Solutions

Production-patterned AI systems: retrieval pipelines, document intelligence, multi-agent orchestration, AI governance as architecture, and provider-agnostic model integration.

What I've Built

Five public repositories demonstrating hands-on AI/ML engineering alongside leadership experience. All systems include evaluation from Day 1, no framework dependency, and AI governance as a first-class architectural concern.

rag-pipeline
Multi-agent RAG with retrieval, grounded generation, dual-mode evaluation, and built-in governance: prompt injection detection, PII redaction, autonomy tiers, token budgets, and structured audit logging.
View on GitHub →
97% retrieval
99% faithfulness
doc-intelligence
Schema-driven document classification, structured field extraction, validation, and cross-document analysis. Adding a new document type means adding a JSON file, not writing new code.
View on GitHub →
100% classification
100% extraction
consulting-mcp-server
MCP server exposing both pipelines as 8 tools consumable by any MCP-compatible AI client. Protocol-level interoperability tying multiple AI capabilities into a single composable surface.
View on GitHub →
8 tools
25 tests
agentic-audit
End-to-end agentic workflow automating the consulting data audit discovery process. Interview question generation through answer synthesis with factual vs anecdotal classification.
View on GitHub →
6 agents
full pipeline
llm-adapter
Provider-agnostic LLM abstraction layer. Swap providers by changing one line in a config file. Supports Anthropic, OpenAI, Azure, Bedrock, and local models.
View on GitHub →
5 providers
pip-installable

Education and Certifications

Executive Certificate, Machine Learning in Business
Massachusetts Institute of Technology (MIT)
BS, Computer and Information Systems (Information Security)
Strayer University
Project Management Professional (PMP)
Project Management Institute
ITIL Certified
IT Service Management

Let's Talk

Based in Richmond, VA. Available for consulting engagements, advisory work, and leadership opportunities in AI/ML engineering.