Lead the applied research vertical of a team building an agentic AI platform, and build the production retrieval and evaluation systems it runs on.

Responsibilities

• Lead the team’s applied research vertical, with a research agenda centered on agentic orchestration and execution-grounded evaluation
• Prototyping agentic execution, in which models draft and execute plans, use structured errors to revise them, and recover failed runs by selecting alternative plans
• Redesigned and shipped the platform’s document retrieval system, combining filename-aware routing with content retrieval across document families and versions
• Built the evaluation layer for the platform’s LLM-generated SQL, measuring the correctness, stability, and groundedness of both queries and their executed results
• Re-engineered the group’s evaluation suite for concurrent execution, cutting a full run from about 3 hours to about 8 minutes
• Built an internal tool that provisions a vLLM inference endpoint for any model in about 30 seconds, now used across the group

Technologies & Skills

• Agentic systems, retrieval-augmented generation, and LLM evaluation
• Python, LangGraph, and vLLM