Vetted JavaScript Professionals

Stephen Koo

Screened

Senior Software Engineer specializing in LLM infrastructure and AI inference platforms

New York, NY8y exp

“Google Workspace AI engineer who owned major AI assistant infrastructure end-to-end: React/TypeScript UI, a Node.js context-aggregation gateway on Cloud Run, and a Go inference layer on GKE serving Gemini. Built and productionized Gmail RAG + agentic workflows with rigorous evals and guardrails, and has a strong track record of measurable impact (latency, engagement, acceptance-rate lifts) and zero-incident migrations using feature flags/strangler patterns across multiple ML teams.”

“Backend/data engineer with deep healthcare data experience (FHIR, de-identification) across both GCP and AWS. Has built and operated production microservices and ETL pipelines (FastAPI, Dataflow, Glue) with strong reliability practices, and led modernization of a legacy SAS compliance reporting system to cloud services with validated parity and stakeholder-facing Looker comparisons.”

Kaiqi Zhang

Screened

Senior Full-Stack Engineer specializing in cloud-native SaaS and AI applications

Bellevue, WA13y exp

“ML/LLM engineer with hands-on experience shipping production RAG systems at Google Clinical Search and GenAI recommendation/summarization features in Meta Ads. Stands out for combining research-to-production execution with rigorous grounding, evaluation, safety checks, and reusable Python platform components that improved both reliability and team velocity.”

“Hands-on engineering leader currently at Vianai (GenAI enterprise applications) building AI-assisted data exploration/insights products with Python and React. Previously Corporate VP of Digital Health at Samsung Electronics, where they helped close key partner deals by demonstrating API-driven openness and led a major Samsung Health modernization from monolith to microservices for horizontal scalability and faster, safer iteration.”

“Engineering leader/player-coach who built and validated a preference-based recommendation engine using clustering, including generating test data to evaluate how clusters evolve over time. Has SRE/DevOps experience and has owned production incidents end-to-end (logging-driven RCA and refactoring patterns that failed at large data scale). Emphasizes quality and platform stability via unit, integration, and load testing, and has managed performance via regular 1:1s and PIPs.”