I will build a backend API in python go or node js

U
utkarshg7
U
utkarshg7
Utkarsh G
build a backend API in python go or node js
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build a backend API in python go or node js
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build a backend API in python go or node jsbuild a backend API in python go or node js

About this gig

Most backends work fine until real traffic arrives. Then the edge cases show up: the job that ran twice, the webhook that fired late, the count that quietly drifted wrong.


That is the work I have spent 20 months on. Founding engineer on a voice AI platform running live customer calls, where a duplicate callback or a lost job meant real money and real complaints.

WHAT I BUILD

REST APIs with clean endpoints, authentication, and a database schema that will not fight you in six months. Integrations with third-party services: webhooks, rate limits, pagination, tokens that expire at the worst moment.

Queues and background workers where it matters: retries, idempotency, and jobs that must never run twice. I built a durable execution engine with atomic step-claim for exactly that reason.

STACK

Python (FastAPI, Django), Go, Node.js (Express). PostgreSQL, Redis, AWS, Docker.

TRACK RECORD

Migrated realtime services from Node.js to Go, reaching roughly 4,000 concurrent calls at peak. Fixed three production races at the root, including a duplicate callback inflating counts 2x.

You get full source code, API docs and a walkthrough.

  • Programming language

    • Go

  • Expertise

    • Algorithms & Data structures
    • Debugging
    • Performance
    • Security
    • Databases

  • Backend framework

    • Django
    • Express.js
    • FastAPI
    • Node.js

Get to know Utkarsh G

Utkarsh G

AI Engineer building LLM Agents RAG Chatbots and Voice AI

  • FromIndia
  • Member sinceOct 2024

  • Languages

    Hindi, English
I build AI agents that survive real users, plus the backend that keeps them running. 20 months shipping production AI at two startups, not demos. What I build: - LLM chatbots and agents with tool calling against your live data - RAG pipelines with guardrails and evaluation harnesses - Voice AI: real-time agents, speech-to-text, latency tuning, Hindi/English - Backends in Python and Go: APIs, WebSockets, workflow engines Results: cut an AI copilot's token cost 17%, lifted answer accuracy 40% on a multilingual pipeline, scaled a call platform to 4,000 concurrent calls.

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