AI Systems Engineer

LUCKY
PATEL

|

4+

Years

15+

Systems Shipped

6+

AI Products

Let's BuildView Systems
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RAG Systems/LLM Apps/AI Agents/Agentic Workflows/CrewAI/LangGraph/Next.js/Node.js/React/TypeScript/Python/GraphQL/MongoDB/OpenAI/LangChain/Vector DBs/AWS/RAG Systems/LLM Apps/AI Agents/Agentic Workflows/CrewAI/LangGraph/Next.js/Node.js/React/TypeScript/Python/GraphQL/MongoDB/OpenAI/LangChain/Vector DBs/AWS/

About Me

I build AI systems
that ship to production.

I'm Lucky Patel, an AI Systems Engineer with 4+ years of full-stack development experience. I design and build production AI applications: RAG pipelines, LLM-powered products, and intelligent systems that solve real problems at scale.

As the co-founder of Mystiq Technologies, I've shipped AI integrations, chat systems, and data platforms end-to-end, from architecture to deployment. I work across the full stack: Node.js, Next.js, React, GraphQL, MongoDB, and the entire modern AI toolchain.

lucky@ai-systems ~

// current stack

$ echo $CORE_STACK

RAG · AI Agents · LLMs · Next.js · Python

$ echo $APPROACH

"Retrieval before generation."

"Ship fast, observe, iterate."

"Production-first architecture."

$

AI Agents

Multi-agent systems & workflows

Agentic AI

CrewAI, LangGraph, tool use

RAG Systems

Retrieval & embedding pipelines

Shipping

End-to-end AI products

Services

What I do.

LLM Application Development

Production RAG systems, AI agents, and LLM-powered products. From retrieval pipelines to streaming chat interfaces.

RAGLangChainOpenAIAgents

Full-Stack Product Engineering

End-to-end product development with modern frameworks. Real-time systems, APIs, and scalable architecture.

Next.jsNode.jsGraphQLMongoDB

AI Integration & Pipelines

Embed AI into existing products. Transcription, analysis, content generation, and intelligent automation pipelines.

APIsPipelinesStreamingAutomation

Production AI Infrastructure

Ship AI systems that handle real traffic. Vector databases, embedding pipelines, and optimized inference at scale.

PineconeAWSDockerRedis

Real-Time Systems

WebSocket-based platforms, live dashboards, and event-driven architectures for applications that demand instant data.

Socket.ioWebSocketRedisEvents

AI Product Strategy

Technical consulting on where AI fits in your product. Architecture decisions, MVP scoping, and build-vs-buy analysis.

ArchitectureMVPLLM StrategyRoadmap

Skills

Engineering toolkit.

AI & LLM Systems

LLMs

GPT-4, Claude, Llama, Gemini

RAG Systems

Vector search, embeddings, retrieval pipelines

AI Agents

Autonomous agents, tool use, multi-step planning

LangChain

Chains, agents, memory, tools

LangGraph

Stateful agent graphs, conditional routing

CrewAI

Role-based agent teams, collaborative workflows

Prompt Eng.

Few-shot, CoT, system design

Vercel AI SDK

Streaming, tool calling, AI routes

Fine-tuning

LoRA, PEFT, dataset curation

Tool Use

Function calling, API orchestration, MCP

OpenAI APIs

Chat, Assistants, Function calling, Vision

Frontend

React.js

Hooks, context, server components

Next.js

App router, SSR, ISR, middleware

TypeScript

Generics, utility types, strict mode

JavaScript

ES2024, async patterns, Web APIs

Tailwind

Custom themes, animations, responsive

Three.js

3D rendering, WebGL, animations

Backend

Node.js

Express, Fastify, streams

Python

FastAPI, async, data pipelines

GraphQL

Apollo, resolvers, subscriptions

REST APIs

Design, auth, rate limiting

Socket.io

Real-time bidirectional communication

WebSockets

Live data, event-driven architecture

Data & Infra

MongoDB

Aggregation, Atlas, Mongoose

PostgreSQL

Prisma, Drizzle, migrations

Vector DBs

Pinecone, Weaviate, ChromaDB

Redis

Caching, pub/sub, queues

Supabase

Auth, realtime, edge functions

Firebase

Firestore, auth, cloud functions

Embeddings

OpenAI, Cohere, sentence-transformers

DevOps

AWS

Lambda, S3, EC2, Bedrock

Docker

Compose, multi-stage builds

Vercel

Edge functions, analytics, deployments

CI/CD

GitHub Actions, automated pipelines

Git

Branching strategies, CI workflows

Linux

Shell scripting, systemd, networking

Projects

Systems I've built.

ChatGenius

Production RAG assistant that ingests company documents and answers questions with source citations. Custom retrieval pipeline with vector search, chunking strategies, and streaming LLM responses.

  • 10K+ documents indexed
  • <800ms time-to-first-token
  • 95% citation accuracy
Next.jsOpenAIPinecone+2

AI Interview Analyst

End-to-end system that transcribes interview recordings, extracts key insights, and generates structured analysis reports. Audio processing, speaker diarization, and LLM-powered summarization.

  • 60min audio → report in <2min
  • 90%+ speaker diarization accuracy
  • Structured scoring across 12 dimensions
PythonWhisperGPT-4+2

Semantic Search Engine

Document search platform converting unstructured text into vector embeddings with contextually relevant retrieval. Multi-tenant indexing, hybrid keyword + semantic search, and relevance tuning.

  • 100K+ documents indexed
  • <500ms p95 retrieval latency
  • 30% relevance lift over keyword-only
OpenAI EmbeddingsPineconeNext.js+2

AI Content Pipeline

Automated content generation system running inputs through LLM chains for drafting, editing, and formatting. Human-in-the-loop review and version tracking built in.

  • 3-stage LLM chain
  • 70% reduction in drafting time
  • Full version diff audit trail
LangChainGPT-4Redis+2

Multi-Agent Research Bot

Agentic workflow system where specialized AI agents collaborate to research topics, gather data, cross-verify facts, and produce comprehensive reports. Role-based agent teams with shared memory.

  • 4 specialized agents coordinated via LangGraph
  • 85% fact-check agreement rate
  • Reports generated in <5min
CrewAILangGraphOpenAI+2

Zintly

Full-stack printing platform connecting customers with print vendors. Real-time order tracking, file upload processing, vendor management, and automated pricing engine.

  • Real-time WebSocket order tracking
  • Multi-vendor pricing in <200ms
  • Print-ready file validation pipeline
ReactNode.jsMongoDB+2

+ many more

System Design

RAG Pipeline Architecture.

How I build production retrieval-augmented generation systems. Click any node to explore the implementation details, or watch the data flow automatically.

inference.log
01

User

Query Input

02

API Gateway

Request Orchestration

03

Embedding Model

Semantic Encoding

04

Vector Database

Similarity Search

05

Retriever

Context Assembly

06

LLM

Generation Engine

07

Streaming Response

Real-Time Output

Latency

< 200ms

P95 retrieval + generation with streaming first-token delivery

Throughput

10K+ RPM

Horizontal scaling with async pipelines and connection pooling

Accuracy

95%+

Grounded responses with hybrid search, reranking, and citation validation

Streaming Response — token-by-token

Design Principles.

Modularity

Each pipeline stage is independently deployable and testable. Swap embedding models or vector DBs without rewriting the system.

Observability

Every retrieval and generation step is traced. Latency, relevance scores, and token usage are logged for continuous improvement.

Graceful Degradation

If the retriever returns low-confidence results, the system falls back to the LLM's parametric knowledge with clear attribution.

Evaluation-Driven

Automated evals with RAGAS metrics (faithfulness, answer relevancy, context precision) run on every pipeline change.

Contact

Let's build something real.

Building an AI product, need a technical co-builder, or want to integrate LLMs into your stack? I ship production AI systems. Let's talk.

Location

Ahmedabad, Gujarat, India