You will get a multi-agent AI system that automates complex tasks
Project details
I build multi-agent AI systems where specialized AI agents collaborate to complete complex tasks not a single chatbot, but a coordinated system where one agent researches, another decides, and another acts.
Most AI tools handle one task at a time. A multi-agent system mimics how a real team works: each agent has a specific role, reasons independently, and hands off to the next step enabling AI to handle workflows too complex for a single prompt-response bot.
What I offer:
Multi-agent architecture using LangGraph (ReAct) or CrewAI
Agents that reason, use tools, and act autonomously
Memory/context handling across multi-agent workflows
API, database, and tool integration (Twilio)
Full testing for reliability before handoff
My process: Understand your use case → design agent roles & handoffs → build & connect tools → test accuracy → deliver with documentation.
I've built a production agentic AI system using LangGraph's ReAct architecture that reasons over conversations and autonomously decides which tool to invoke including taking a real, irreversible action (a live phone call) with zero human approval.
Message me your use case and I'll confirm the right architecture before order
Most AI tools handle one task at a time. A multi-agent system mimics how a real team works: each agent has a specific role, reasons independently, and hands off to the next step enabling AI to handle workflows too complex for a single prompt-response bot.
What I offer:
Multi-agent architecture using LangGraph (ReAct) or CrewAI
Agents that reason, use tools, and act autonomously
Memory/context handling across multi-agent workflows
API, database, and tool integration (Twilio)
Full testing for reliability before handoff
My process: Understand your use case → design agent roles & handoffs → build & connect tools → test accuracy → deliver with documentation.
I've built a production agentic AI system using LangGraph's ReAct architecture that reasons over conversations and autonomously decides which tool to invoke including taking a real, irreversible action (a live phone call) with zero human approval.
Message me your use case and I'll confirm the right architecture before order
AI Algorithms
Convolutional Neural Network, Large Language Model, Linear Discriminant Analysis, Long Short-Term Memory Network, Transformer ModelAI Applications
AI Chatbot, Image ProcessingAI Development Language
PythonAI Tools
GitHub Copilot, Hugging Face, PyTorch, Streamlit, TensorFlowAI Models
ChatGPT, GPT-3, GPT-4, GPT-Neo, LLaMAWhat's included
| Service Tiers |
Starter
$50
|
Standard
$100
|
Advanced
$150
|
|---|---|---|---|
| Delivery Time | 1 day | 3 days | 4 days |
Number of Revisions | 1 | 3 | 5 |
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 Sahil
AI/ML Engineer | Python | LLMs, AI Agents, LangChain, LangGraph, RAG
Sukkur, Pakistan - 1:39 am local time
I build custom ML/DL models, deploy GenAI/LLM features, and turn messy data into decisions your team can act on.
𝗠𝘆 𝗖𝗼𝗿𝗲 𝗦𝗲𝗿𝘃𝗶𝗰𝗲𝘀
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𝗣𝗿𝗲𝗱𝗶𝗰𝘁𝗶𝘃𝗲 𝗠𝗼𝗱𝗲𝗹𝗶𝗻𝗴 & 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀
Classification, regression, forecasting, and clustering models (scikit-learn, XGBoost) built on your real data, cleaned, validated, and explained so you can trust the output.
𝗗𝗲𝗲𝗽 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗦𝗼𝗹𝘂𝘁𝗶𝗼𝗻𝘀
Computer vision and NLP models (PyTorch, TensorFlow) for image classification, object detection, text classification, and sequence tasks, trained and tuned for your actual use case.
𝗟𝗟𝗠 & 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝘃𝗲 𝗔𝗜 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻
RAG pipelines, AI chatbots, and OpenAI/LangChain-powered features built into your product, grounded in your data instead of hallucinating.
𝗗𝗮𝘁𝗮 𝗣𝗶𝗽𝗲𝗹𝗶𝗻𝗲𝘀 & 𝗘𝗗𝗔
End-to-end data cleaning, feature engineering, and exploratory analysis so your models are only as good as the data behind them, and the data is solid.
𝗠𝗼𝗱𝗲𝗹 𝗗𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁 & 𝗔𝗣𝗜𝘀
Turning trained models into usable, production-ready APIs (Flask/FastAPI) so your ML isn't stuck in a Jupyter notebook.
𝗢𝗻𝗴𝗼𝗶𝗻𝗴 𝗔𝗜/𝗠𝗟 𝗦𝘂𝗽𝗽𝗼𝗿𝘁
Retraining, monitoring, and improving models as your data and business evolve, not a one-time delivery.
𝗦𝗸𝗶𝗹𝗹𝘀 & 𝗘𝘅𝗽𝗲𝗿𝘁𝗶𝘀𝗲
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🧠 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴: Scikit-learn | XGBoost | Random Forest | Regression | Classification | Clustering
🤖 𝗗𝗲𝗲𝗽 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴: PyTorch | TensorFlow | Keras | CNNs | RNNs/LSTMs | Transfer Learning
💬 𝗟𝗟𝗠 / 𝗚𝗲𝗻𝗔𝗜: OpenAI | LangChain | RAG | Prompt Engineering | Hugging Face
📊 𝗗𝗮𝘁𝗮 & 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀: Pandas | NumPy | Matplotlib | Seaborn | SQL | Power BI/Tableau
⚙️ 𝗗𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁: Flask | FastAPI | Docker | Git | Streamlit
🗄️ 𝗗𝗮𝘁𝗮𝗯𝗮𝘀𝗲𝘀: MySQL | PostgreSQL | MongoDB
𝗪𝗵𝘆 𝗠𝗲?
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Real, working models delivered not just theory or tutorials
Clear communication and honest timelines from day one
Strong focus on clean, explainable, production-ready models over black-box guesses
Comfortable across the full pipeline: data cleaning, modeling, deployment
I treat your data and problem like my own project, not just a gig
I take ownership of the problem you're solving, not just the task assigned. When you work with me, you get someone who digs into your data, explains tradeoffs honestly, and delivers something that actually works on your real-world data, not just a clean demo.
🔑 𝗙𝗶𝗻𝗱 𝗺𝗲 𝘄𝗶𝘁𝗵 𝘁𝗵𝗲𝘀𝗲 𝗸𝗲𝘆𝘄𝗼𝗿𝗱𝘀
Data scientist, machine learning engineer, deep learning, AI engineer, predictive modeling, data analysis, Python, scikit-learn, TensorFlow, PyTorch, NLP, computer vision, LLM, OpenAI, LangChain, RAG, AI chatbot, data pipeline, model deployment, forecasting, classification
Steps for completing your project
After purchasing the project, send requirements so Sahil can start the project.
Delivery time starts when Sahil receives requirements from you.
Sahil works on your project following the steps below.
Revisions may occur after the delivery date.
Use Case & Workflow Review
I understand what tasks need to be split across agents and what decisions each agent should make
Agent Role & Architecture Design
I define each agent's role, tools, and how they hand off tasks to one another.