# Hire an AI agent and LLM developer

URL: https://shahid09.is-a.dev/hire/ai-agent-developer/
Author: Muhammad Shahid, Senior AI Software Engineer & Backend Developer (https://shahid09.is-a.dev/)

Production AI agents, RAG over your own data and LLM features inside your product, built with the backend, monitoring and tests they need to keep working.

**Quick answer:** I build AI agents, multi-agent systems and RAG pipelines with LangGraph, LangChain, LlamaIndex and n8n, on the OpenAI and Anthropic (Claude) APIs, and the Node.js or Python backend around them. I am currently building the AI product platform Joni (joni.ai), and I hold the Build AI Agents using LangGraph certification.

## What I build

- **AI agents and multi-agent systems:** Agents that plan, call your tools and APIs, and hand work to each other, coordinated with LangGraph.
- **RAG over your data:** Chunking, embeddings and vector search, so answers are grounded in your documents and data.
- **LLM features in your product:** Chat, summaries, extraction and AI assistants plugged into your existing backend.
- **Workflow automation:** n8n workflows and agents that remove repetitive work between your tools.
- **Evaluation and monitoring:** Test sets before release; tracing, quality, latency and cost tracking in production.
- **Cost and latency control:** Caching, smaller models where quality allows, trimmed context and streaming.

## Results on real products

- **Joni (joni.ai):** AI product platform — My main ongoing project at Quecko Inc.: I build the backend services, agent workflows and integrations that make the AI product work.
- **SnapText and Picture to Text:** On-device AI — Text recognition that runs entirely on iPhone and Mac, so images never leave the device. Picture to Text reads 30+ languages.
- **Resume Builder – CV Maker AI:** AI in a shipped app — An App Store app with an AI assistant that improves resume text and exports ATS-friendly PDFs.

## Tools I use

LangGraph, LangChain, LlamaIndex, n8n, OpenAI API, Anthropic API, RAG, Vector search, Python, Node.js, TypeScript, AWS, Docker

## How a project works

1. **Scope call.** We talk through the goal, the current state of the product and what success looks like.
2. **Written plan.** You get fixed milestones, delivery dates and a clear price before any work starts.
3. **Build in small pieces.** I ship in small steps with regular progress updates, so you always see what is built.
4. **Tested delivery.** Delivery includes tests, documentation, handover and free support afterwards if something breaks.

## Frequently asked questions

### Which AI models do you work with?

I build on the OpenAI and Anthropic (Claude) APIs, with retries, timeouts, streaming, rate-limit handling and usage logging, and I choose the model per feature based on quality, speed and cost.

### Can you add AI to our existing product?

Yes. I build API and LLM integrations that plug into your current backend, tools and data, so the AI feature works with what you already have.

### Can the AI answer from our own documents?

Yes. That is what RAG is for: I index your content with embeddings and vector search, and the model answers from the retrieved context instead of guessing.

### How do you keep LLM costs under control?

I cache repeated answers, use smaller models where quality allows, trim prompts and context, batch background work, and track cost and latency per feature.

### How do you know the AI is good enough to ship?

I evaluate every change against test sets before release, then monitor answer quality, latency and cost in production.

### Can you build an AI MVP from scratch?

Yes. I scope it with you, plan fixed milestones, and build the AI layer, the backend and the app, so you deal with one person.

## Tell me what you want to build

Send the details on WhatsApp or by email: what you need, where the project is now, your deadline and budget range. You get a clear plan and price back, usually within 24 hours.

WhatsApp +92 321 7815799 · contacttoshahidkhan@gmail.com · Upwork: https://www.upwork.com/freelancers/~014a63f2391f4f4bce