Vetted Multi-Agent Systems Professionals

“Recent graduate who independently built scholar.ai from scratch in roughly two weeks, shipping a full multimodal ingestion, retrieval, and grounded Q&A system for students. Also created Anchor SDK, an open-source framework for runtime hallucination detection and recovery, showing unusually strong depth in production LLM systems, evals, and reliability for an early-career candidate.”

“Hands-on automation and QA-focused developer using AI agents, MCP tools, and LLMs to streamline business workflows. Built agents for automated Jira bug logging, executive summary dashboards, and a rule-explainer that translates technical business rules into plain language for end users, while also supporting Selenium-to-Playwright migration and guiding peers on AI implementation.”

Nechar KC

Screened

Entry-level AI Engineer specializing in full-stack LLM applications

Hayward, CA1y exp

“Built an AI-powered lecture-to-notes web app from personal pain at university, combining Spring Boot, React, PostgreSQL, and Python AI services for transcription and summarization. Also led architecture for FishBowl, a multi-agent Shark Tank-style pitch practice tool using GPT-4o, Perplexity, and ElevenLabs that won 1st place in the AI & Entrepreneurship track at HackHayward.”

“Built and deployed an AI customer representative for iCore used at the IEE convention (2025), serving 100+ users in a day; implemented RAG with a vector database and scaled reliability via Docker and Google Cloud. Also has hands-on experience with multiple agent orchestration stacks (LangChain/LangGraph, Google AI Agent Development Kit, OpenAI SDK, Composio) and has delivered stakeholder-driven apps using prototyping and MVP scoping.”

Richa Chanpuriya

Screened

Mid-level Backend/Agentic AI Engineer specializing in GenAI automation and RAG systems

Remote, India3y exp

“Built and shipped a production AI-driven privacy automation system that autonomously navigates data broker sites to submit opt-out/data deletion requests end-to-end, including robust CAPTCHA detection/solving (e.g., reCAPTCHA/hCaptcha/Cloudflare) via 2Captcha. Experienced in orchestrating stateful LLM agent workflows with LangGraph and hardening them for production with strict state management, retries/fallbacks, validation layers, and database-backed observability/audit logs, collaborating closely with legal/compliance stakeholders.”

“AI/LLM engineer who has owned end-to-end production delivery of multi-agent RAG systems on Azure (React + FastAPI + data pipelines + Terraform), including rigorous evaluation/monitoring and reliability guardrails. Shipped an AI-driven observability root-cause analysis assistant that reduced MTTR ~30%, cut alert noise ~20%, and reached ~70% adoption in the first month; also built a clinical document Q&A system with citations and compliance-oriented controls.”