AI Engineer 路 Technical Trainer at Revature 路 Developer Educator
I help developers and teams turn AI capabilities into systems they can understand, evaluate, and adopt. I build production-shaped prototypes across LLM applications, RAG, agentic workflows, data, and cloud鈥攁nd turn the hard parts into clear demos, practical labs, and reusable engineering patterns.
I've mentored 500+ engineers, with a focus on making emerging technology approachable without hiding its tradeoffs, failure modes, or production boundaries.
| Project | What it demonstrates |
|---|---|
| AI Engineering Notebook | A validated builder's notebook for LLM applications, RAG, agents, evals, integrations, and AI product design |
| FFXI AI Agents Lab | A local-first agent lab with bounded MCP control, fail-closed safety, observable actions, and live YouTube gameplay streams |
| Support Triage Review Console | A customer-facing AI workflow with bounded model calls, exact-schema validation, human review, safe failure handling, and layered cost controls |
| Agentic Workflow Demo | Typed tool contracts, approval-gated actions, refusal behavior, structured traces, and behavioral evals |
| Prompt Regression & Feedback Pipeline | Fixed-case prompt comparison plus an explicit path from human corrections to reviewed candidate evals |
| Enablement Assistant RAG | Grounded answers with source-aware retrieval, citations, refusal behavior, and an 18-case evaluation suite |
| Mocked Support Adapter | Signed webhooks, replay protection, PII redaction, strict mapping, and proposed-only customer-system updates |
| StreamFlow Analytics Platform | Streaming and analytics architecture across Spark, Airflow, Snowflake-style modeling, BI semantics, and reconciliation checks |
| Data Analytics Learning Lab | Synthetic, source-backed product analytics practice with decision-ready reports and reproducible evidence |
| HTML-in-Canvas Lab | Emerging browser capability explored through original demos, progressive enhancement, and accessible fallbacks |
| Portfolio | A focused view of my projects, working principles, technical range, and public contact points |
- Start with the real workflow: connect technical choices to a user, operating constraint, and measurable outcome.
- Make trust inspectable: expose sources, schemas, traces, evals, approval boundaries, and failure behavior.
- Teach the whole system: turn architecture and debugging decisions into demos, labs, tests, rubrics, and reference implementations.
- Name the production boundary: distinguish a compelling prototype from what identity, privacy, observability, reliability, and scale still require.
- AI developer enablement, solution architecture, and technical storytelling
- Eval-driven LLM applications, RAG, and tool-using agents
- Human-in-the-loop workflows, safety boundaries, and observability
- Data engineering, analytics, and distributed systems
- Developer tooling, technical curriculum, and accessible web experiences
Python 路 TypeScript 路 Java 路 OpenAI API 路 React 路 Next.js 路
Spring Boot 路 PostgreSQL 路 BigQuery 路 PySpark 路 Airflow 路 Docker 路
AWS 路 GCP
Independent projects and views are my own.