Vetted Software Developers

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.”

“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.”

“Fintech product builder focused on AI-native opportunities; recently helped their company operationalize LLMs in customer service and bank ops by creating systems to continuously improve prompts and evaluations. Also drove growth by identifying hooks in a major American retailer distribution channel and integrating into the retailer’s payment flows to become a key part of the user funnel.”

Vernon Quan

Screened

Senior Full-Stack Software Engineer specializing in TypeScript, React, and Node.js

Menlo Park, CA7y exp

“Meta engineer who owned a heavily used internal goals/performance management feature end-to-end, driving major responsiveness improvements by restructuring frontend data loading and adding backend caching (Memcached), cache warmups, and query/index tuning. Strong focus on production quality: observability, error monitoring, and improving test coverage to prevent regressions during peak review cycles.”

“Engineering/tech leader with experience spanning major platforms (mentions Microsoft and AWS) and leading ML-driven product architecture (ranking systems). Describes driving a shift to a layered, API-standardized service architecture (including vector database components) that materially increased delivery speed (weeks to hours) while improving scalability and security, and emphasizes rigorous execution reviews (QDR/OKRs) plus transparent remote-team operating rhythms.”

“Operator-turned-aspiring founder with experience building CI, cloud, and AI businesses at Harness, including P&L ownership and 0-to-1 execution. Exploring an AI-native infrastructure/control-plane thesis focused on moving enterprise workflows from AI assistance to execution, with a strong emphasis on ROI, coordination gaps, and durable moats.”