Global Master Trainer – OpenAI Enablement · AI Engineer · Developer Educator
I help developers, technical teams, partners, and business stakeholders identify where AI can create value, translate those opportunities into practical solutions, and build the skills and operating patterns needed to adopt them responsibly.
I currently work as a Global Master Trainer – OpenAI Enablement at Channel Partners Solutions, delivering technical and role-based training for OpenAI customers and partners. My work spans foundational AI knowledge, OpenAI products and APIs, Codex, deployment, solution application, cybersecurity, and partner enablement.
A core part of that work is consultative: clarifying business problems and workflows, identifying stakeholders and success criteria, qualifying AI opportunities, framing credible first use cases, and helping teams think through deployment readiness, governance, controls, and responsible enterprise adoption.
Alongside enablement and consultative work, I build production-shaped prototypes across LLM applications, RAG, agentic workflows, data, and cloud—and turn the hard parts into clear demos, practical labs, evals, and reusable engineering patterns.
I've mentored 500+ engineers, with a focus on making emerging technology approachable without hiding its tradeoffs, failure modes, governance needs, 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 |
- Qualify before prescribing: understand the business problem, workflow, stakeholders, constraints, evidence, and definition of success before jumping to a technical solution.
- Start with the real workflow: connect technical choices to a user, operating constraint, and measurable outcome.
- Translate between business and technical context: help stakeholders move from a broad AI opportunity to a concrete, reviewable solution shape.
- Make trust inspectable: expose sources, schemas, traces, evals, approval boundaries, and failure behavior.
- Teach the whole system: turn architecture, deployment, governance, and debugging decisions into demos, labs, tests, rubrics, and reference implementations.
- Adapt to the audience: move between engineers, customer-facing teams, administrators, partners, and business stakeholders without losing technical accuracy.
- Name the production boundary: distinguish a compelling prototype from what identity, privacy, observability, reliability, security, and scale still require.
- OpenAI product, API, and Codex enablement
- AI opportunity discovery, workflow qualification, solution framing, and stakeholder alignment
- Enterprise AI adoption, deployment readiness, governance, and responsible use
- Eval-driven LLM applications, RAG, MCP, and tool-using agents
- Human-in-the-loop workflows, safety boundaries, observability, and review controls
- Technical facilitation, hands-on labs, solution storytelling, and partner enablement
- Developer tooling, data systems, and production-shaped AI prototypes
Python · TypeScript · Java · OpenAI API · OpenAI Codex · MCP ·
FastAPI · React · Next.js · Spring Boot · PostgreSQL · BigQuery ·
PySpark · Airflow · Docker · GitHub · AWS · GCP
Independent projects and views are my own.