Avinish Kumar, back to top
Currently: Core Platform @ Leena AI

Backends that run AI agents.

Backend engineer building AI agents at scale: distributed systems, real-time streaming, message queues, zero-downtime migrations, and multi-region AWS.

3+
years shipping backend
60-80%
cheaper code review with AI agents
10x
faster delivery with AI tooling

About

I work on the Core Platform at Leena AI, where the systems I build carry every conversation between an enterprise employee and an AI agent: the streaming runtime, the token auth, the channel integrations. When a message survives a pod restart or a dropped socket, that is my code doing its job quietly.

I build most of it with Claude at the center of my workflow. The review pipelines, test generators, and code search tools I wrote for myself now run for my whole team. Before this I was an SDET at Cognizant, which is why I still write code assuming someone will try to break it. Along the way I earned an M.Tech in AI and Data Science from IIT Patna.

How an agent turn works

What happens between a user pressing send and an AI agent's reply streaming back. Each bar is one stage of the pipeline I build and operate; they fire in the order shown.

trace: one agent turn
ws: message receivedthe user hits send
auth: session token (JWT refresh)verify who is asking
runtime: build context + promptassemble the conversation
llm: stream response tokensthe model thinks, token by token
tool: execute + return resultthe agent takes an action
sse: deliver to clientthe reply streams back, replayed if the socket drops

Selected work

Agent Dashboard

An AI operator agent inside the admin console. Streams responses over SSE, executes tools against platform APIs, and lets teams configure and debug bots conversationally.

ClaudeSSENode.js
2026

Multi-agent PR review pipeline

A tiered pipeline of review agents with adversarial verification. Cut review cost 60-80% and turnaround from hours to minutes.

Claude APITypeScript
2026

Streaming conversation runtime

Real-time SSE and WebSocket backbone serving hundreds of enterprises: reconnect message replay, server-driven UI, and delivery reliability across multi-region AWS.

WebSocketsRabbitMQMongoDB
2025

Enterprise channel integrations

Shipped WhatsApp v2, MS Teams provisioning, external live-chat handoff with RFC 7662 token introspection, and SAML SSO, end to end.

OAuthSAMLREST
2025

Semantic code search MCP

Natural-language code search across 9 repositories, built on tree-sitter parsing, BM25, and vector embeddings.

MCPEmbeddings
2025

Design system & micro-frontends

A 30+ component design system and micro-frontend framework used by 25+ engineers across hundreds of enterprise deployments; monorepo migration cut build times 50%.

ReactStorybook
2023

Skills

Backend

  • Node.js
  • TypeScript
  • MongoDB
  • RabbitMQ
  • Kubernetes
  • Docker
  • SSE / WebSockets
  • Microservices

AI & LLM

  • Claude API
  • Claude Code
  • MCP
  • Multi-agent pipelines
  • RAG & embeddings
  • Agentic workflows

Frontend

  • React
  • Next.js
  • Redux
  • React Query
  • Tailwind
  • Motion

Experience

Jul 2022 - Present

Software Engineer, Core Platform at Leena AI

Own the conversation runtime and agent auth behind an enterprise AI agent platform. Led a 948-error type-safety migration, shipped WhatsApp, MS Teams, and live-chat integrations, and run L3 support and releases for 3 product modules.

Node.jsTypeScriptMongoDBRabbitMQKubernetesClaude
Feb 2022 - Jul 2022

Software Engineer Intern at Cognizant

Migrated a legacy BackboneJS frontend to React and built end-to-end test automation that cut manual QA cycles.

ReactJavaScriptSelenium

Education

2024 - 2025

Indian Institute of Technology, Patna

M.Tech, Artificial Intelligence & Data Science Engineering

2018 - 2022

Chandigarh Engineering College

Bachelor of Technology, Computer Science

Certifications

MongoDB certified: Building AI Agents, AI-Powered Search with Vector Search, RAG Apps

Writing

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mr.avinish@gmail.com