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amir4javar/README.md

Hi, I'm Amir Hossein Javartani 👋

AI Engineer | NLP, RAG Systems, and LLM Applications

I help teams and clients turn documents and research ideas into production-ready AI systems — especially multi-turn RAG assistants, hybrid retrieval pipelines, and deployable LLM backends.

🟢 Available for freelance / contract work
📧 amir4javar@gmail.com


What I Build for Clients

  • Custom RAG chat assistants over PDFs / internal docs
  • Hybrid retrieval systems (vector + keyword search)
  • Multi-turn conversational AI with session memory
  • LLM backends with FastAPI, WebSocket streaming, and Docker deployment
  • Model efficiency work (distillation / optimization) when performance or cost matters

If you need an AI feature that is more than a notebook demo, I can design, implement, and ship it.


Featured Project

Live demo: Hugging Face Spaces

A deployable document Q&A system, not a toy chatbot:

  • Multi-turn conversations with context-aware follow-ups
  • Intelligent query routing (skip unnecessary retrieval)
  • Hybrid search via Weaviate (dense vectors + BM25)
  • Real-time token streaming over WebSockets
  • FastAPI backend + Streamlit UI
  • Auth / persistent sessions (Supabase)
  • Docker deployment, automated tests, evaluation harness, optional tracing

Stack: LangGraph · Weaviate · FastAPI · Streamlit · Docker · OpenAI-compatible LLMs

Client use cases: internal knowledge bots, technical document assistants, policy/manual Q&A, research PDF copilots


Selected Work


Technical Toolbox

Area Tools & Frameworks
ML / NLP PyTorch · Hugging Face Transformers · Fine-tuning · Knowledge Distillation · LLM APIs
RAG & Agents LangChain · LangGraph · Weaviate · Hybrid Search (vector + BM25)
Backend & MLOps FastAPI · Docker · Linux · REST / WebSocket APIs · vLLM / Ollama
Frontend Streamlit
Quality Pytest · Evaluation (RAGAS / DeepEval) · Observability (OpenTelemetry / Phoenix)

How I Can Help

I take freelance projects in:

  1. RAG / document assistants (MVP → production)
  2. LLM application development (API + UI + deployment)
  3. Retrieval quality improvements (chunking, hybrid search, routing, evals)
  4. Efficient ML (distillation / optimization) when relevant

Typical engagement styles:

  • fixed-scope MVP
  • production hardening of an existing prototype
  • short contract to ship a specific AI feature

Get in Touch

If you have a project that needs an AI engineer who can ship:

Open to freelance, contract, and strong full-time opportunities in applied AI / NLP.

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