Vetted API Design Professionals

shuntoria reid

Screened ReferencesModerate rec.

Senior AI Trainer and Data Annotator specializing in LLM and computer vision datasets

Greenwood, SC11y exp

“Early-career software engineer with a blended background in technical support, QA, data work, and hands-on web development. Built QA-tracker-style applications and small AI-enabled prototypes using Python, Flask, SQL, JavaScript, and React, with a strong emphasis on testing, debugging, and turning messy requirements into practical user workflows.”

AungKaung Khant

Screened

Junior Software Engineer specializing in full-stack web and IoT development

Cupertino, CA3y exp

“Computer Science student and indie hacker who builds open-source products in public, including a Chrome extension called "Add to ChatGPT" that reached 30+ installs in its first week. Has hands-on experience with full-stack social platform development and AI-integrated side projects, with a strong pattern of self-teaching, rapid shipping, and iterative bug fixing.”

“Builder of an early-stage community marketplace, Cirkle, where they combined product and engineering ownership to ship core marketplace, membership, chat, and AI-assisted listing features. Particularly strong in pragmatic AI product development, using multiple LLMs, structured outputs, validation, and analytics to create production-ready user experiences.”

“Built a solo full-stack equipment manager app for the game Dragon's Dogma, scraping a wiki with Beautiful Soup, transforming data to JSON, modeling armor/weapon schemas, and integrating everything into a PostgreSQL-backed API with caching. Dockerized and deployed the application to the web.”

Muhammad Zeeshan

Screened ReferencesModerate rec.

Entry Full-Stack Developer specializing in MERN, Next.js, and AI integrations

Rahim Yar Khan, Pakistan1y exp

“Full-stack TypeScript engineer who has owned features from React UI through Express APIs to MongoDB persistence, with hands-on production support after launch. Stands out for improving reliability and engineering speed through shared types, standardized API contracts, service-layer architecture, and practical database performance fixes like indexing and pagination.”

“Backend engineer who built and evolved a low-latency document search platform (C++/gRPC on Kubernetes with a vector database), emphasizing resilience under concurrent load through strict deadlines, retries, idempotency, and observability. Also experienced building secure, frontend-friendly FastAPI services (Pydantic + JWT) and executing safe incremental refactors using feature flags and parallel validation.”

“Full-stack developer who has built both operational software for universities and an AI-assisted publishing platform called HeartThreads. Notably implemented a multi-LLM fallback architecture using Ollama, OpenAI, and Claude to keep AI story-generation features available in production.”

“Frontend/full-stack engineer who built a fast-food ordering web app and a minimal one-page real-time admin dashboard (Supabase + Postgres) with role-based access. Emphasizes scalable architecture, strict TypeScript validation, and quality practices (error boundaries, backend validation, unit testing), and has experience refactoring messy codebases into reusable components with consistent Tailwind theming.”

“Full-stack JavaScript developer (Node + React) who built a CRM system end-to-end, including fine-grained access control (RBAC plus per-user permissions) and user activity tracking. Currently focused on performance optimization for a data-heavy web app, applying lazy loading, image compression, and pagination to dramatically reduce load times, and has implemented per-user API rate limiting to control request bursts.”

Rutvi Rathod

Screened

Junior Full-Stack Software Engineer specializing in AI and web applications

San Jose, CA1y exp

“LLM/AI backend engineer with hands-on experience taking customer LLM prototypes into production using FastAPI, containerization, CI/CD, and OpenTelemetry-based observability. Demonstrated measurable impact by cutting LLM costs ~40% and reducing workflow errors ~50% through schema-enforced outputs, better tool definitions, retries, and prompt/model optimization; also supports pre-sales via technical discovery and rapid integration demos.”