I build the pipelines that make AI products actually work in production
Senior Technical Program Manager repositioning toward AI Product Management — hands-on with RAG pipelines, MCP integrations, and agent tooling that ships, not just prototypes.
Agent and retrieval systems, shipped.
Prompt Dojo
→A gamified, belt-ranked prompt-engineering trainer, built as a single dependency-free HTML file
AI News Digest
→A scheduled research agent that reads, verifies, and remembers — so the daily AI news summary never repeats itself or pads a quiet day
Job Hunter
→A resume-driven job search skill that deep-matches listings on a weighted rubric and discloses coverage gaps instead of hiding them
Graph-Engineer: DAGs of Sub-Agents
→A Claude Code skill that turns a prompt into a validated graph of parallel sub-agents instead of one long serial run
Jira Triage Agent
→An LLM agent that reads tickets, searches a shared knowledge base, and cross-references logs before an engineer ever opens the ticket
From technical program management into hands-on AI product work.
I'm a Senior Technical Program Manager at Kantar, a B2B2C survey platform serving enterprise clients. I lead AI product initiatives end to end — from OGSP planning and multi-region launches to the retrieval and agent infrastructure that makes those products work.
Over the past year: a shared RAG knowledge base on pgvector, agents wired into real systems of record through MCP, and an autonomous investigation agent I call Jira-Bug-Investigation.
- Agents Claude, MCP, Claude Code, Codex
- Retrieval pgvector, OpenAI embeddings, New Relic, S3
- Systems Atlassian, GitHub, Figma, Lucid MCPs
- Program OGSP planning, RICE prioritization, multi-region launch
- Analytics Python, SQL, Tableau, Power BI