Open to full-time roles

Hi, I’m Snehel Basu

AI backend Engineer — LLM systems, multi-agent workflows, and Python backends

I work mostly on the backend of AI products — multi-agent orchestration, retrieval pipelines over knowledge graphs, and FastAPI services built to stay maintainable as they scale. Most recently I built a Graph-RAG knowledge engine and a multi-tenant chatbot platform at Zaito AI, and I care about clean architecture and getting the boring parts right.

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01

Projects

A few things I have designed and shipped lately — products, open source, and one experiment that never quite became a product.

  • 01

    Multi-Agent Research System

    A LangGraph pipeline that runs several agents in parallel to plan, research, and write a report on a topic. Redis and Celery handle the job queue, SSE streams progress live, and E2B sandboxes any code the agents generate.

    • LangGraph
    • Celery
    • Redis
    • SSE
    • E2B
    2026 · AI toolingView on GitHub
  • 02

    AI Trading Arena

    Several AI agents paper-trade perpetual futures independently. A pipeline derives technical indicators such as MACD and RSI and feeds them to the model as context before it places a trade, through the Lighter SDK.

    • Python
    • LLM
    • Lighter SDK
    2026 · Applied AIView on GitHub
  • 03

    RAG Job Matching Platform

    Recruiters post a role and immediately get a ranked list of matching candidates. Built on RAG best practices — query rewriting and metadata filtering — to keep the matches accurate. Won second prize at the CodeConquer ’25 hackathon.

    • RAG
    • Vector DB
    • Metadata filtering
    2025 · Hackathon winnerView on GitHub
02

Experience

Where I have worked, what I owned, and what shipped because of it.

  1. Nov 2025 — Jun 2026

    Graph-RAG & Knowledge Graph Engine · Zaito AI

    Backend Developer Intern

    • Built an automated collection pipeline that scrapes news articles and web content into an internal AI knowledge graph.
    • Designed a multi-agent extraction pipeline that identifies entities and selective relationships in raw text, turning unstructured documents into connected graphs.
    • Developed the Graph-RAG natural language search layer, letting users query graph data and get back contextual nodes, relationships, and cited answers.
    • Integrated third-party APIs such as HubSpot and engineered the data pipeline to onboard over 10k existing CRM records into the knowledge base.
  2. Nov 2025 — Jun 2026

    Multi-Tenant Chatbot-as-a-Service · Zaito AI

    Backend Developer Intern

    • Architected the platform from scratch on FastAPI and PostgreSQL, with an agency dashboard for tracking chats, leads generated, and reviews.
    • Engineered customisation controls so agencies can tailor chatbot styling, configure custom Q&A datasets, and build dynamic lead-capture forms.
    • Implemented domain whitelisting so the widget validates origin domains before serving, preventing unauthorised site-key usage on unapproved sites.
  3. Nov 2025 — Jun 2026

    Title Insurance Assistant · Zaito AI

    Backend Developer Intern · Core product

    • Contributed to the flagship AI assistant, building document tagging so users can query precisely across selected real estate title documents.
    • Built the pipeline that generates a property's initial report in a customisable format with PDF export.
    • Executed a large-scale Qdrant migration, re-embedding thousands of production records to move the vector store from OpenAI to Google embeddings.
03

Skills

The tools I reach for most, grouped by what I actually use them for.

Languages

Python and TypeScript carry most of my work, with JavaScript, Rust, and Java on the side.

AI & orchestration

LangGraph, LangChain, and PydanticAI for multi-agent systems, tool use, and typed agent flows.

RAG & retrieval

Graph-RAG and vector search across Qdrant and Neo4j, including large-scale re-embedding migrations.

Backend & APIs

FastAPI, Express, and Node.js behind REST and WebSocket APIs, with a bias toward clean architecture.

Async & infrastructure

Redis, Celery, and SSE for job queues and live progress streaming; Docker, Postgres, and Alembic for delivery.

Data & integrations

Web scraping pipelines, third-party API integrations such as HubSpot, and onboarding messy data into knowledge bases.

04

Interests

Mysteries, mostly. Japanese detective fiction, locked-room puzzles, and one web novel that rewired how I think about worldbuilding.

  • Lord of the MysteriesCuttlefish That Loves Diving
  • The Inugami CurseSeishi Yokomizo
  • Strange HousesUketsu
  • The Devil's Flute MurdersSeishi Yokomizo
  • And Then There Were NoneAgatha Christie
  • The Murder of Roger AckroydAgatha Christie
  • Days At The Morisaki BookshopSatoshi Yagisawa
  • Murder on the Orient ExpressAgatha Christie

Hover the shelf to stop it. 8 books, mostly detective fiction.

05

Let’s build something together.

basusnehel789@gmail.com

Open to full-time roles — in AI or backend engineering at an early-stage startup, or a project worth building together.