Context
An AI company needed 15 legal domain experts to annotate court transcripts for training a legal-focused LLM.
The timeline was tight: complete recruitment, task execution, and delivery within 3 weeks — with full visibility and quality tracking.
This simulation explores how I would manage the entire pipeline as a Technical Project Manager.
The Problem
The challenge was to recruit niche domain experts quickly, evaluate their capabilities accurately, and structure task execution with clear checkpoints — all while ensuring weekly client updates and on-time payments. With no internal engineering support, the entire operation needed to be built using low-code, scalable tools that allowed for full visibility, accountability, and speed.
My Solution
I built a modular expert management pipeline using lightweight, no-code tools to ensure speed and clarity. ChatGPT and Tally were used to generate role descriptions and collect expert applications, while Canva table tracked candidate progress across stages like shortlisting, testing, and onboarding. Task briefs, QA checklists, and weekly updates were organized in Notion, with a manual evaluation system in place to verify domain expertise. Payment tracking was integrated directly into the workflow, creating a fully remote, transparent, and scalable system without relying on engineering support.
Task Tracker Table (Canva Mock)
Sample grid view representing candidate stages, application dates, assessments, and real-time status—showcasing organized, actionable data.
Communication Plan
Throughout the project, I would maintain alignment between clients and contributors through structured, low-friction communication. Weekly progress updates would be shared via Notion, while expert onboarding would be streamlined using pre-written documentation and emails. Task progress would be tracked directly in Canva, and automated check-ins would be scheduled using simple forms or reminder tools — ensuring clarity without unnecessary back-and-forth.
Outcome Highlights
The simulated pipeline delivered strong outcomes: 3 domain experts were successfully hired, and all completed tasks passed QA checks with 100% accuracy. Payments were tracked and released on time, creating a seamless, low-code operational flow that required no engineering support.