You will get Generative AI, LLM & Machine Learning Development with Python & LangChain
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Top Rated
Project details
Build production-grade Generative AI, LLM, and Machine Learning solutions
using Python, LangChain, LangGraph, and AWS Bedrock — from model
development to intelligent automation pipelines.
With expertise across the full AI/ML stack, I deliver solutions that are
fast, accurate, and built to scale — LLMs, computer vision, or end-to-end
AI products.
What I build:
→ LLM & Generative AI — GPT/Claude/Gemini, prompt engineering, fine-tuning,
RAG pipelines, multi-agent systems (LangGraph)
→ Computer Vision — image classification, object detection, YOLO, OpenCV
→ NLP & Text AI — text classification, sentiment analysis, NER, BERT/GPT
→ ML Development — model design, training, evaluation, optimization
→ Data Science — data cleaning, EDA, feature engineering, forecasting
→ MLOps — model serving, FastAPI, Docker, AWS/GCP deployment
Deliverables by tier:
1. Starter: Single ML model — data prep, training, source code, docs
2. Standard: Full pipeline — model + API + deployment + testing
3. Advanced: End-to-end AI system — LLM/agents, MLOps, cloud deploy
Tech: Python · LangChain · LangGraph · AWS Bedrock · TensorFlow · PyTorch
· Hugging Face · OpenAI/Claude · Scikit-learn · FastAPI
using Python, LangChain, LangGraph, and AWS Bedrock — from model
development to intelligent automation pipelines.
With expertise across the full AI/ML stack, I deliver solutions that are
fast, accurate, and built to scale — LLMs, computer vision, or end-to-end
AI products.
What I build:
→ LLM & Generative AI — GPT/Claude/Gemini, prompt engineering, fine-tuning,
RAG pipelines, multi-agent systems (LangGraph)
→ Computer Vision — image classification, object detection, YOLO, OpenCV
→ NLP & Text AI — text classification, sentiment analysis, NER, BERT/GPT
→ ML Development — model design, training, evaluation, optimization
→ Data Science — data cleaning, EDA, feature engineering, forecasting
→ MLOps — model serving, FastAPI, Docker, AWS/GCP deployment
Deliverables by tier:
1. Starter: Single ML model — data prep, training, source code, docs
2. Standard: Full pipeline — model + API + deployment + testing
3. Advanced: End-to-end AI system — LLM/agents, MLOps, cloud deploy
Tech: Python · LangChain · LangGraph · AWS Bedrock · TensorFlow · PyTorch
· Hugging Face · OpenAI/Claude · Scikit-learn · FastAPI
AI Algorithms
Convolutional Neural Network, Gated Recurrent Unit, Generative Adversarial Network, Large Language Model, Long Short-Term Memory Network, Multimodal Large Language Model, Recurrent Neural Network, Regression Analysis, Transformer Model, YOLOAI Applications
AI Chatbot, AI Mobile App Development, AI Text-to-Speech, AI-Generated Code, AIOps, Anomaly Detection, Automatic Speech Recognition, Conversational AI, Image Recognition, Natural Language Generation, Sentiment Analysis, Text RecognitionAI Development Language
PythonAI Tools
Azure OpenAI, GitHub Copilot, Gradio, Hugging Face, Microsoft 365 Copilot, NVIDIA AI Platform, PyTorch, Streamlit, TensorFlowAI Models
BERT, ChatGPT, DALL-E, GPT-4, LLaMA, OpenAI Codex, Stable Diffusion, WhisperWhat's included
| Service Tiers |
Starter
$500
|
Standard
$1,200
|
Advanced
$2,500
|
|---|---|---|---|
| Delivery Time | 5 days | 10 days | 15 days |
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 |
Optional add-ons
You can add these on the next page.
Fast Delivery
+$200 - $600Frequently asked questions
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SM
Syed M.
Jul 29, 2026
Full-Stack AI Engineer for Multi agent workflow
EB
Ed B.
Jan 14, 2026
Technical Architecture & Strategy Brief for a Multi-Modal AI Platform
Muhammad demonstrated outstanding technical depth and strategic thinking on the Unbound AI Architecture project. His two-LLM design elegantly solves complex fine-tuning trade-offs, and his model selection (Qwen 3.1-14B) shows deep understanding of production requirements. The comprehensive data sourcing strategy, infrastructure planning, and deployment roadmap are all production-grade. Muhammad thinks like a senior architect, not just an implementer. Highly recommended for complex AI/ML projects. Would work with again
SM
Syed M.
Dec 20, 2025
AI Chatbot Development for Writing Assistance
"Mudassir is a true expert in their field. They understood the technical requirements immediately and executed the task with precision and attention to detail. The final result exceeded my expectations. I will definitely be hiring them again for future projects."
ES
Erik S.
Nov 10, 2025
Senior LangGraph Engineer - Agentic AI Workflows
Great developer!
SM
Syed M.
Nov 4, 2025
AI Chatbot Development for Writing Assistance
Throughout the project, Muhammad maintained clear and transparent communication, providing regular updates and seeking clarification whenever needed. This not only kept the project on track but also fostered a collaborative and efficient working relationship.
About Muhammad
AI Agent & Chatbot Developer | LangChain, LangGraph, FastAPI & Python
100%
Job Success
Attock City, Pakistan - 5:12 pm local time
I work with SaaS founders, product teams, and businesses that need to build a new AI product, add Generative AI to existing software, automate operational workflows, or improve an AI system that is not reliable enough for production.
My work covers the complete AI application stack from LLM architecture and retrieval pipelines to backend APIs, frontend applications, integrations, evaluation, and cloud deployment.
WHAT I BUILD
→ AI Agents & Agentic Workflows
AI agents that can reason across multi-step workflows, call tools and APIs, maintain state and memory, route tasks, use structured outputs, and involve human approval where required.
Typical stack: LangGraph, LangChain, OpenAI, Claude, Gemini, AWS Bedrock, FastAPI, Python, REST APIs, CRMs, n8n, and custom business systems.
→ RAG Chatbots & Knowledge Systems
RAG applications for private documents, company knowledge bases, customer support, enterprise search, document Q&A, and internal AI assistants.
I handle document ingestion, chunking, embeddings, metadata filtering, vector search, hybrid retrieval, reranking, citations, access control, evaluation, and retrieval optimization.
Typical stack: LangChain, LlamaIndex, Pinecone, Qdrant, pgvector, FAISS, PostgreSQL, OpenAI, Claude, Gemini, and AWS Bedrock.
→ AI Integration & Workflow Automation
Integrate AI directly into existing SaaS platforms, CRMs, internal applications, APIs, databases, and operational workflows.
Examples include:
Support and ticket automation
Lead qualification
Document processing
Research and data workflows
CRM automation
Internal approval workflows
AI-assisted operations
API-driven business automation
The goal is not another standalone chatbot. It is AI connected to the systems where the actual work happens.
→ Full-Stack AI SaaS Development
Complete AI-powered SaaS products and internal applications, including:
Next.js / React frontend
Python / FastAPI backend
PostgreSQL / Supabase / MongoDB
Authentication and role-based access
Stripe or subscription billing
Admin dashboards
Background processing
LLM usage tracking
API integrations
Docker and cloud deployment
Suitable for AI SaaS MVPs, internal AI platforms, vertical AI products, and new AI features inside existing applications.
→ Document Intelligence
AI pipelines for extracting, classifying, searching, summarizing, and analyzing PDFs and business documents.
Applications include contracts, invoices, reports, tenders, policies, knowledge bases, forms, and other unstructured business data.
Depending on the workflow, this may combine OCR, vision models, structured extraction, RAG, validation rules, and human review.
→ Voice AI
Voice agents and conversational workflows for customer support, lead qualification, appointment workflows, and outbound or inbound automation using platforms such as Vapi, Twilio, and ElevenLabs.
→ LLM Evaluation & Optimization
For existing AI products, I can improve the underlying system rather than rebuilding everything from scratch.
This can include:
Prompt and context optimization
Retrieval evaluation
Agent debugging
LangSmith tracing
Structured output validation
Guardrails and fallback logic
Latency optimization
Token and API cost optimization
Model comparison
Fine-tuning when it is actually justified
TECHNICAL STACK
AI / LLM: OpenAI, Claude, Gemini, AWS Bedrock, Hugging Face, LangChain, LangGraph, LlamaIndex, CrewAI
RAG / Vector Search: Pinecone, Qdrant, pgvector, FAISS, Chroma, embeddings, hybrid search, reranking
Backend: Python, FastAPI, Node.js, REST APIs, WebSockets, Celery, Redis
Frontend: Next.js, React, TypeScript, Tailwind CSS
Data: PostgreSQL, Supabase, MongoDB, Redis
Automation & Integrations: n8n, APIs, webhooks, CRM integrations, third-party SaaS integrations
Cloud & Deployment: AWS, Azure, GCP, Docker, Vercel, CI/CD
PRODUCTION ENGINEERING
AI prototypes are relatively easy to build. Production systems become difficult when real users, private data, unreliable APIs, latency, permissions, and edge cases enter the workflow.
That is where I focus.
Depending on the system, I design for:
Evaluation and testing
Source-grounded responses
Structured outputs
Retry and fallback logic
Human-in-the-loop approval
Authentication and authorization
Logging and observability
Cost and latency control
Maintainable architecture
Production deployment
A GOOD FIT IF YOU NEED TO
Build an AI agent or multi-agent workflow from scratch, add RAG or Generative AI to an existing SaaS application, automate a business workflow using AI, build an AI SaaS product, connect LLMs with your APIs and private data, or diagnose an AI system that works in demos but struggles in production.
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.
Preprocessing
Analysis, Cleaning and Preparation of Dataset. This may include Data Visualization and feature Extraction Techniques.
Model Development
Developing a model based on feature extraction done at preprocessing stage.