Dear Hiring Manager,
Most AI demos look magical and most AI products quietly disappoint, and the gap between them is engineering. At Quillstone I took a promising prototype assistant and made it dependable enough to put in front of paying customers, with evaluations and guardrails behind every answer. Turning model capability into a product people trust is the work I want to bring to your team.
An AI engineer has to be equal parts builder and sceptic, and I hold both. I design retrieval and prompting pipelines that ground answers in real data, I build evaluation harnesses so we measure quality rather than vibe check it, and I add the guardrails, fallbacks and cost controls that a production feature needs. I treat a language model as one unreliable component in a system I am responsible for making reliable.
At Quillstone I built the evaluation and retrieval layer for our customer support assistant. By introducing a graded test set and a retrieval rerank step, I lifted answer accuracy from 71 percent to 92 percent on our benchmark while cutting token cost per resolved query by a third. The guardrail layer reduced unsupported or hallucinated responses to under 2 percent, which is what finally cleared the feature for launch.
Your advert mentions taking an AI feature from prototype to production, which is exactly the transition I specialise in. I would enjoy discussing how I would build the evaluation and safety scaffolding around it. Could we set up a short conversation?
Yours sincerely, Ravi Chandran