Vetted Retrieval-Augmented Generation (RAG) Professionals

“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.”

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.”

Manas Kumar Sherla

Screened

Junior Machine Learning Engineer specializing in NLP and LLM-based clinical AI

Charleston, IL2y exp

“Built a production automated resume matching system using Python, FAISS vector search, and Selenium-based job scraping, including mitigation for IP blocking and heterogeneous site structures. Also develops LLM/RAG applications with LangChain, using Pydantic-guardrailed structured outputs and LLM-as-a-judge evaluation (including a project focused on tone/semantics for a 3D avatar’s emotional responses).”

Nikunj Baraiya

Screened

Mid-level Unity Game Developer specializing in multiplayer and AI-driven gameplay

Bhavnagar, India4y exp

“Unity developer who has shipped mobile and Meta Quest experiences, including a real-time audio collaboration app using Unity + Photon Fusion. Demonstrates end-to-end gameplay ownership plus production LLM integration (Firebase-delivered quests) with measurable latency/cost improvements and a disciplined A/B + analytics-driven iteration loop; also experienced debugging live multiplayer issues (desyncs, NAT failures) with tools like Wireshark.”

“Backend engineer who built and scaled AmazonProAI, a multi-tenant SaaS platform for Amazon sellers, using a modular Django/DRF monolith with strict seller-level isolation and security controls. Led a controlled SQLite-to-PostgreSQL migration and hardened bulk Excel ingestion with idempotency and data integrity constraints to prevent duplicate metrics and noisy alerts while keeping the system ready for future service extraction.”

“Backend-focused developer who built LinguaTile (language learning app) on a FastAPI + MongoDB monolith deployed to Google Cloud Run, emphasizing async performance and security (RBAC/JWT, rate limiting, request tracing). Also created Mark-RS, a static HTML generator with a 100% CommonMark-compliant Markdown parser, demonstrating strong edge-case rigor and systems robustness.”

“Built FlowPilot, an AI-powered product that generates complete importable n8n workflows from natural-language prompts using a RAG pipeline (Qdrant + LangChain) and a multi-stage agent with a scoring/repair 'Judge' loop for intent alignment. Experienced in backend architecture across Laravel/Node microservices and production AI/RAG systems, plus performance debugging from async job offloading to database index tuning after ORM migrations.”

Anjali Chepuri

Screened

“Built and deployed an LLM-powered financial document processing and summarization platform at Morgan Stanley using a production RAG pipeline (PDF ingestion, embedding-based retrieval, schema-constrained JSON outputs) delivered via FastAPI microservices on Kubernetes. Drove measurable impact (40% reduction in manual review time) and improved factual accuracy for numeric fields by 30% through metadata-aware retrieval, strict schemas, and post-generation validation, with a human feedback loop from financial analysts.”

“Built multiple AI projects end-to-end as a solo developer, including a privacy-focused LLM app that redacts PII before sending prompts to an external model and a LangGraph-based multi-agent triage system for log analysis. Stands out for combining LLM/agent design, deployment troubleshooting, and practical workflow automation with a strong emphasis on privacy and explainability.”