GenAI Engineer · Backend Architect · LLM Systems
Hey, I'm Priyanshu Patel — I work at the intersection of LLMs, backend infra, and cloud deployment. Not just prompts. The whole system.
Production-grade RAG pipelines, vector search, embedding strategies, and LLM orchestration that go beyond demos into real deployments.
Scalable APIs, microservices, async queues, and database design built to handle production loads with observability baked in.
CI/CD pipelines, containerized deployments, and cloud-native infra that keep AI products running reliably at scale.
Multi-agent systems, autonomous task orchestration, tool use, memory architectures, and LLM eval frameworks.
ETL pipelines, vector databases, hybrid search, and data prep workflows that feed production AI systems reliably.
React and Next.js frontends that surface AI capabilities in clean, usable interfaces — because UX matters for adoption.
I'm a GenAI + backend engineer obsessed with taking AI from Jupyter notebooks into real production systems. I believe the model is 10% of the product — the infra, pipelines, observability, and deployment are everything else.
I've built RAG pipelines serving real users, agentic workflows that orchestrate multi-step tasks autonomously, and backend APIs that scale under load.
"The model is 10% of the product. The infra around it is everything."
Built a full-stack Chrome Extension enabling semantic Q&A over YouTube videos using a RAG pipeline. Integrated transcript ingestion, vector search, and LLM-based answer generation with timestamp grounding.
Production-ready platform for discovering wellness centers, booking experts, and tracking health activities. Built with scalable backend architecture, OAuth authentication, and optimized PostgreSQL queries.
Real-time AI voice agent for matrimonial platforms using Sarvam STT/TTS with structured intent extraction. Designed low-latency conversational pipeline with WebSockets, caching, and decision engine.
India's green infrastructure and carbon credit lifecycle platform. Multi-portal SPA architecture serving project developers, corporate ESG teams, and FPOs — covering credit issuance, retirement workflows, BRSR/GRI/CDP report generation, and marketplace.
Chunking strategies, re-ranking, hybrid search, and the retrieval failures nobody talks about until they hit 10k users.
A deep-dive into the infra, observability, and reliability patterns that turn LLM experiments into production systems.
Planner-executor-critic patterns, tool reliability, memory persistence, and avoiding the failure modes of naive agentic loops.