Vetted Vercel Professionals

“Solo developer who built and operated an AI debate product end-to-end, from architecture and deployment through observability and post-launch stabilization. They show strong practical LLM production experience—using Vercel AI SDK, OpenAI, Langfuse, Mem0, and custom RAG—while improving latency to sub-4 seconds, driving failures near zero, and cutting LLM usage by 20%.”

“Built and deployed “ApartmentFinder AI,” a multi-agent system using Google ADK, Gemini, and Google Maps MCP to automate apartment shortlisting and commute-time analysis, cutting a 45–70 minute user workflow down to ~30 seconds. Also has strong delivery/process chops from serving as an SDLC Release Coordinator, managing 52+ releases and reducing SDLC issues by 84%.”

“Full-stack TypeScript/React/Next.js builder who created an end-to-end customer-facing product (AI Job Master) that generates personalized outreach from resumes and job descriptions. Demonstrates strong product + engineering ownership with rapid MVP iteration, instrumentation-driven prioritization, and pragmatic reliability patterns (microservices, queues, correlation IDs, retries) while tackling a key AI challenge: user trust and output consistency.”

“Full-stack engineer who built a checklist configuration/task execution system using Next.js App Router + TypeScript, with a React Native app consuming the execution UI via WebView. Was the only full-stack developer at a very small startup (CTO/CFO/CEO team), owning feature delivery plus client-facing on-call debugging, and has hands-on Postgres modeling and query optimization experience.”

“Built a production 13-step LLM agent pipeline at Sentari AI that converts diary entries into personalized AI responses and a persistent structured user profile. Stands out for a pragmatic approach to production AI: strict schemas, function calling, state-machine orchestration, confidence thresholds, logging, and validation layers to control non-determinism and prevent bad profile updates.”