Vetted Retrieval-Augmented Generation (RAG) Professionals

“Healthcare product leader with deep oncology and pharma experience who has built both foundational clinical data platforms and AI-powered patient engagement workflows. Particularly notable for combining rigorous data standardization, LLM evaluation/guardrail design, and human-centered communication changes that drove measurable medication refill and adherence improvements. Also teaches AI to product managers and business analysts, bringing a pragmatic, education-first view of human-centered AI adoption.”

“Engineering leader and hands-on architect from Sony Pictures Entertainment who has led large-scale platform modernization, digital supply chain delivery, and multiple GenAI/RAG initiatives in media and rights-management domains. Notable for combining people leadership with deep AWS/LLM architecture work, including a 1M+ record GenAI title search system and AI-driven contract insights that reportedly helped unlock a 20% revenue increase.”

“25+ year professional exploring entrepreneurship; previously helped develop corporate venture capital at Siemens and AVEVA and ran end-to-end diligence through deal close, with heavy technical diligence on pre-seed/pre-revenue companies. Interested in building an AI-enabled hard lending/origination approach to reduce decision-to-close time and scale in a large lending market, and prefers VC studio/EIR models with paid roles rather than equity-only arrangements.”

“Built a production LLM-powered root cause analysis agent for supply chain alerts that helped operations managers avoid manual dashboard investigation. Demonstrates unusually strong depth in agent reliability, orchestration, and observability, with concrete production practices like hallucination blocking, shadow testing on 500 cases, and data-driven improvements that raised user agreement to 94% while cutting GPT-4 usage by 60%.”

“Backend-focused engineer with Walmart Global Tech experience building shipment and seller workflow systems using Spring Boot, GraphQL, Kafka, and async processing. Stands out for improving bulk label API performance by 60-75%, designing item-level partial-failure workflows that improved user clarity, and also exploring AI-powered debugging/RCA platforms with Java, Python, LangChain, and LLM integrations.”

“Engineer focused on AI-powered developer automation and agent-driven software delivery, with experience spanning customer-facing edtech and internal tooling. They describe building a K-12 chatbot-based learning platform for the York Region public school system and creating internal automations like code-generation pipelines and diff summarization tools adopted across teams, alongside work on legacy encrypted messaging systems at Instagram/Meta.”