AI ENGINEER · ISLAMABAD · UTC+5
AI and full-stack engineer with about four years building and operating production LLM systems. My lane is the reliability side — agents and MCP, including proactive, tool-using assistants on OpenClaw and Hermes — plus RAG, and the evals, observability and cost work that keep those systems honest, with a sharper edge in AI-search visibility (GEO).
Three things worth knowing.
MapleScholar is live and I am its only engineer — paste a paper’s link, DOI or title and read it as a clean web page, then discuss it in your own language out of 70+. I also rebuilt a nonprofit’s site after it failed Google’s Ad Grants review; the resubmitted application was approved.
Read how it was built →I compared ten vision models on one fixed test set, then ran the winner over 14,105 real documents for $18.15 — about a tenth of a cent each. On a live platform I found a monitoring blackout that had hidden production errors for months, and a billing error overcharging three to five times on some models.
See the pipelines →I spent two and a half months testing whether text on a page could be matched the way DNA sequences are. It failed, and I established why rather than quietly dropping it. The retrieval system I built answers “no match” instead of inventing one — checked against a deliberately unanswerable query across all 15 candidates.
How I measure that →A live agent grounded in the full corpus of my projects, incidents, and decisions. Pick a question below or type your own — it answers only from my record, and says so when it doesn't know.
