You will get Efficient PHP, JavaScript, Laravel, MySQL, and API Integration services
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Project details
This project stands out by delivering scalable SaaS solutions using PHP, Laravel, MySQL, JavaScript, and API Integration. My focus is on building reliable, secure, and high-performing platforms tailored to the education industry, ensuring long-term value and ease of use.
Programming Languages
PHP, JavaScript, TypeScriptWhat's included
| Service Tiers |
Starter
$500
|
Standard
$1,000
|
Advanced
$1,500
|
|---|---|---|---|
| Delivery Time | 4 days | 3 days | 2 days |
Number of Revisions | 0 | 1 | 2 |
Number of Pages | 2 | 3 | 4 |
Design Customization | - | - | - |
Content Upload | - | - | - |
Responsive Design | - | - | - |
Source Code | - | - | - |
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Spencer A.
May 21, 2026
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Great working with Faisal. Smart and straightforward communicator.
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Ben H.
Aug 6, 2025
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Really great to work with Faisal, he did a solid job working with us and helped us through some difficult problems.
Thanks for the help and we would gladly work with him again!
Thanks for the help and we would gladly work with him again!
TM
Tanya M.
May 30, 2025
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Sean E.
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About Faisal
AI Engineer | LLM Agents, RAG & MCP | Python + TypeScript | Automation
100%
Job Success
The Colony, United States - 11:45 pm local time
I am an AI engineer with an Bachlor in Generative AI from the University of Texas at Dallas and a prior background in production software and contact-center systems integration. Over the past year I shipped work across more than 30 repositories, spanning AI agents, RAG systems, and full-stack AI products, including open-source tools published to npm. My work is grounded in real engineering discipline: spec-first design, test-first implementation, and containerized deployment with CI/CD.
What sets my AI work apart is that I treat the model as one component in a system, not the whole system. The LLM proposes, deterministic code decides. That is how I build agents that can take real action without acting dangerously.
💡 What I build
⬡ AI agents and multi-agent systems with CrewAI, AutoGen, LangGraph, and LangChain that coordinate real tasks end to end
⬡ RAG pipelines with LlamaIndex, FAISS, pgvector, and sentence embeddings that ground LLMs in your own knowledge base
⬡ LLM-in-the-loop systems with hard safety boundaries, where a deterministic rule engine authorizes every action the model proposes
⬡ MCP servers that expose your tools to Claude and other agents natively over JSON-RPC
⬡ Workflow automation with n8n, Zapier, and Playwright browser automation that runs unattended with real error handling and retries
⬡ Production APIs in FastAPI, Django, and Flask, and full-stack apps in React 19 and Next.js
⬡ Provider-agnostic LLM integration across OpenAI, Anthropic, Google, Groq, Mistral, and local Ollama models behind one interface
🛠️ Selected work
orch, an open-source CLI published to npm that routes prompts across multiple AI coding agents through a single interface, with budget-aware selection learned from real quota and rate-limit failures
A human-in-the-loop review service for AI-authored documents: a containerized HTTP API plus an MCP server exposing 20 review tools, deployed multi-user behind OAuth
An LLM trading research bot on the Binance test network, where the model reasons over market data through read-only tools and a deterministic risk engine is the only path to an order, verified live and backed by roughly 470 tests
A full job-application platform: a FastAPI agent service that scouts, scores, and tailors applications across six sources, with a provider-agnostic LLM layer and Playwright auto-apply, fronted by a Next.js dashboard
An autonomous server-maintenance agent packaged in Docker, and a Chrome extension that fills web forms from your own documents using a pluggable local-or-cloud AI layer
🧠 Background and research
Generative AI, University Texas at Dallas. My dissertation studies whether large language models actually know what they do not know, probing model calibration and hallucination at knowledge boundaries. I also write about LLM reliability on my engineering blog. Before that I spent years in production software and contact-center systems integration with tools like Cisco Finesse, ActiveMQ, and Jira automation, so I am comfortable both building greenfield AI products and working inside existing enterprise systems.
🛠️ Core stack
Languages: Python, TypeScript, Go, JavaScript
AI: LangChain, LangGraph, CrewAI, AutoGen, LlamaIndex, MCP, OpenAI API, Anthropic API, Gemini, Groq, Ollama, RAG, PyTorch, Hugging Face
Backend: FastAPI, Django, Flask, NestJS, Node.js
Frontend: React 19, Next.js, TypeScript, Tailwind
Data: PostgreSQL, SQLite, FAISS, pgvector, Redis, Firebase
Infrastructure: Docker, Docker Compose, GitHub Actions, CI/CD, Nginx, VPS deployment
If you need an LLM agent, a multi-agent system, a RAG pipeline, or a full-stack AI product built by someone who cares whether it still works next month, send me a message and tell me what you are building. I will tell you honestly whether I am the right fit.
Keywords:
AI Engineer, LLM Developer, AI Agent Development, Multi-Agent Systems, RAG Systems, LangChain, LangGraph, CrewAI, AutoGen, LlamaIndex, MCP, Model Context Protocol, OpenAI API, Anthropic Claude, Python Developer, FastAPI, Django, Next.js, React, TypeScript, LLM Integration, Prompt Engineering, Vector Databases, Workflow Automation, Playwright, Generative AI, Machine Learning, Ollama, AI Automation
Steps for completing your project
After purchasing the project, send requirements so Faisal can start the project.
Delivery time starts when Faisal receives requirements from you.
Faisal works on your project following the steps below.
Revisions may occur after the delivery date.
Requirement Gathering & Access Setup
Collect all project requirements, access credentials, and clarify the development scope with the client.
Architecture & Planning
Plan the SaaS structure, outline modules, and confirm workflows to ensure scalability and performance.