You will get a custom AI agent system with LangGraph to automate your business tasks.
Project details
I build production-ready agentic AI systems using LangGraph and LangChain that reason, remember, and act autonomously on complex business tasks.
Here is exactly how I approach every project:
First I analyze your business workflow and design the complete agent architecture — defining graph nodes, edges, conditional routing, and tool structure before writing any code.
Then I build the core LangGraph stateful pipeline with optimized prompt engineering, short and long-term memory management, and multi-turn conversation handling.
Next I integrate your required external tools — APIs, databases, or live data sources — with dynamic tool calling so the agent decides autonomously which tool to invoke based on context.
I then apply token cost optimization and a response refinement loop to ensure high-quality, reliable outputs while minimizing LLM API costs.
Finally I deliver clean, fully documented source code, deployment-ready architecture, and a live working agent tailored precisely to your business goal.
Proven delivery — built a real-time agentic Prayer Assistant with LangGraph tool calling, Groq LLM, and live deployment on Hugging Face Spaces.
Here is exactly how I approach every project:
First I analyze your business workflow and design the complete agent architecture — defining graph nodes, edges, conditional routing, and tool structure before writing any code.
Then I build the core LangGraph stateful pipeline with optimized prompt engineering, short and long-term memory management, and multi-turn conversation handling.
Next I integrate your required external tools — APIs, databases, or live data sources — with dynamic tool calling so the agent decides autonomously which tool to invoke based on context.
I then apply token cost optimization and a response refinement loop to ensure high-quality, reliable outputs while minimizing LLM API costs.
Finally I deliver clean, fully documented source code, deployment-ready architecture, and a live working agent tailored precisely to your business goal.
Proven delivery — built a real-time agentic Prayer Assistant with LangGraph tool calling, Groq LLM, and live deployment on Hugging Face Spaces.
AI Algorithms
Feedforward Neural Network, Large Language Model, Long Short-Term Memory Network, Multilayer Perceptron, Regression Analysis, Transformer ModelAI Applications
AI Chatbot, AI-Enhanced Classification, Anomaly Detection, Conversational AI, Machine Translation, Natural Language Understanding, Sentiment Analysis, Sequence Modeling, Time Series AnalysisAI Development Language
PythonAI Tools
GitHub Copilot, Hugging Face, Streamlit, TensorFlowAI Models
GPT-3, GPT-4, Naive Bayes Classifier, Stable DiffusionWhat's included
| Service Tiers |
Starter
$80
|
Standard
$150
|
Advanced
$300
|
|---|---|---|---|
| Delivery Time | 5 days | 8 days | 12 days |
Number of Revisions | 2 | 3 | 4 |
AI Model Integration | |||
Batch Normalization | - | - | |
Database Integration | - | ||
Detailed Code Comments | |||
Image Upscaling | - | - | - |
MLOps | - | - | |
Model Deployment | - | - | |
Model Documentation | |||
Model Monitoring | - | - | |
Model Testing & Optimization | - | ||
Model Tuning | - | ||
Natural Language Processing | - | ||
NLP Tokenization | |||
Pre-Training | - | - | |
Prompt Engineering | |||
Setup File | |||
Source Code |
Frequently asked questions
About Muhammad
Machine Learning Engineer | DATA SCIENTIST | RAG | LangGraph | Python
Lahore, Pakistan - 12:52 pm local time
My work goes beyond notebooks. I design, build, and deploy complete ML pipelines and agentic AI systems — from raw data ingestion and feature engineering to model training, optimization, and live deployment. I build agents that reason, remember, and act autonomously using LangGraph stateful workflows, tool calling, and RAG pipelines. Every project I deliver is production-ready, documented, and built to create actual business value.
𝐖𝐡𝐚𝐭 𝐈 𝐡𝐚𝐯𝐞 𝐝𝐞𝐥𝐢𝐯𝐞𝐫𝐞𝐝:
Built a Job Skill Recommender analyzing 1,500+ real job postings using cosine similarity and 93 engineered binary features — deployed live on Azure
Developed a Bank Churn Predictor with a full SMOTE pipeline, reducing false negatives on imbalanced data
Built SmartPricer, a smartphone price prediction app (R²=0.903) deployed with FastAPI
Engineered a production-ready agentic RAG chatbot using LangGraph with stateful tool calling, long-term memory, and Groq LLM integration — deployed on Hugging Face Spaces
Completed a professional NLP internship delivering a text classification pipeline at ~90% accuracy
𝐂𝐨𝐫𝐞 𝐒𝐤𝐢𝐥𝐥𝐬:
𝘗𝘺𝘵𝘩𝘰𝘯 · 𝘗𝘢𝘯𝘥𝘢𝘴 · 𝘚𝘤𝘪𝘬𝘪𝘵-𝘭𝘦𝘢𝘳𝘯 · 𝘕𝘓𝘗 · 𝘓𝘢𝘯𝘨𝘊𝘩𝘢𝘪𝘯 · 𝘓𝘢𝘯𝘨𝘎𝘳𝘢𝘱𝘩 · 𝘙𝘈𝘎 · 𝘍𝘢𝘴𝘵𝘈𝘗𝘐 · 𝘚𝘵𝘳𝘦𝘢𝘮𝘭𝘪𝘵 · 𝘍𝘦𝘢𝘵𝘶𝘳𝘦 𝘌𝘯𝘨𝘪𝘯𝘦𝘦𝘳𝘪𝘯𝘨 · 𝘌𝘋𝘈 · 𝘔𝘢𝘤𝘩𝘪𝘯𝘦 𝘓𝘦𝘢𝘳𝘯𝘪𝘯𝘨
I specialize in turning messy, unstructured data into clean insights and deployed applications — and building intelligent agents that automate complex workflows businesses previously handled manually.
𝐈𝐟 𝐲𝐨𝐮 𝐧𝐞𝐞𝐝 𝐚 𝐫𝐞𝐥𝐢𝐚𝐛𝐥𝐞 𝐌𝐋 𝐚𝐧𝐝 𝐀𝐈 𝐞𝐧𝐠𝐢𝐧𝐞𝐞𝐫 𝐰𝐡𝐨 𝐝𝐞𝐥𝐢𝐯𝐞𝐫𝐬 𝐞𝐧𝐝-𝐭𝐨-𝐞𝐧𝐝 — 𝐧𝐨𝐭 𝐣𝐮𝐬𝐭 𝐜𝐨𝐝𝐞, 𝐛𝐮𝐭 𝐫𝐞𝐬𝐮𝐥𝐭𝐬 — 𝐥𝐞𝐭'𝐬 𝐭𝐚𝐥𝐤.
Steps for completing your project
After purchasing the project, send requirements so Muhammad can start the project.
Delivery time starts when Muhammad receives requirements from you.
Muhammad works on your project following the steps below.
Revisions may occur after the delivery date.
Requirements & Architecture Design
Define agent goal, select optimal LLM, design graph nodes, edges, and tool structure before writing a single line of code.
Core Agent Development
Build the LangGraph stateful pipeline with prompt engineering, conversation memory, context management, and conditional routing logic.