Muhammad is available for hire
Hire MuhammadMuhammad Usman Ali
Verified Expert in Engineering
Machine Learning Developer
Lahore, Punjab, Pakistan
Toptal member since May 1, 2026
Bio
Muhammad is a senior AI professional with 8+ years of experience. He designs and deploys scalable solutions across machine learning, deep learning, computer vision, and generative AI. He's specialized in building intelligent systems leveraging large language models (LLMs), retrieval-augmented generation (RAG), agentic AI, and conversational AI (chatbots) to solve complex real-world problems.
Portfolio
Al Ghurair
Python, WhatsApp, WhatsApp API, Conversational AI, Agentic AI, Webhooks...
Cliquify
Python, RAG Systems, FastAPI, LangChain, PostgreSQL, Vector Databases, Django...
Dough.zone
Python, Computer Vision, Deep Learning, PyTorch, TensorFlow, OpenCV, PostgreSQL...
Experience
- Python - 8 years
- FastAPI - 6 years
- Deep Learning - 5 years
- Machine Learning - 5 years
- Computer Vision - 4 years
- RAG Systems - 4 years
- Generative Artificial Intelligence (GenAI) - 3 years
- AI Chatbots - 2 years
Preferred Environment
Python, Computer Vision, Open-source LLMs, RAG Systems, AI Chatbots, Deep Learning, Machine Learning, Azure Machine Learning
The most amazing...
...projects I've worked involved building AI, LLM, ML, and computer vision solutions.
Work Experience
AI Tech Lead
2025 - PRESENT
Al Ghurair
- Worked as principal AI engineer at Al Ghurair Group, promoted from a vendor-based consulting role leading enterprise AI/GenAI initiatives with McKinsey and IBM, and providing architectural direction.
- Gathered business requirements and produced BRDs/technical specs, then architects end-to-end agentic AI solutions with full design documentation (system architecture).
- Built an autonomous agentic WhatsApp chatbot (decision logic, memory, fallback handling) with full Meta Cloud API integration, working cross-functionally with engineering and product.
Technologies: Python, WhatsApp, WhatsApp API, Conversational AI, Agentic AI, Webhooks, LangChain, Large Language Models (LLMs), Generative Artificial Intelligence (GenAI), MLflow, Artificial Intelligence (AI), Machine Learning Operations (MLOps), Retrieval-augmented Generation (RAG), RAG Pipelines, Agentic AI Systems, Agentic Frameworks, LLM Integration, Orchestration, AI Agents, Model Context Protocol (MCP), Debugging, LangGraph, Amazon Web Services (AWS), AI Chatbots, Azure Machine Learning, AWS Bedrock AgentCore, AWS ECS Fargate
Senior AI Contractor
2023 - 2025
Cliquify
- Engineered a dynamic RAG pipeline that combined vector retrieval with real-time tool use, enabling agents to autonomously query, synthesize, and surface domain-specific recruitment knowledge.
- Designed an agent memory and state management layer using PostgreSQL-backed vector stores, allowing persistent multi-turn context and more adaptive conversational responses.
- Architected and deployed a scalable REST API back end using FastAPI and LangChain/LlamaIndex, enabling production-grade orchestration of tools, knowledge retrieval, and multi-step reasoning workflows.
Technologies: Python, RAG Systems, FastAPI, LangChain, PostgreSQL, Vector Databases, Django, Large Language Models (LLMs), Generative Artificial Intelligence (GenAI), MLflow, Artificial Intelligence (AI), Machine Learning Operations (MLOps), Retrieval-augmented Generation (RAG), RAG Pipelines, Agentic AI Systems, Agentic Frameworks, LLM Integration, Orchestration, AI Agents, Model Context Protocol (MCP), Debugging, LangGraph, Amazon Web Services (AWS)
Computer Vision Engineer
2021 - 2023
Dough.zone
- Implemented deep learning-based detection and segmentation algorithms on road imagery, enabling automated analysis of surface conditions and improving report generation workflows.
- Applied LLMs to summarize feasibility reports and combined them with statistical road-feature analysis to produce more comprehensive and decision-ready final assessments.
- Led ML developers across the full product lifecycle, from data collection and model training to cloud deployment through REST APIs for production use.
Technologies: Python, Computer Vision, Deep Learning, PyTorch, TensorFlow, OpenCV, PostgreSQL, Artificial Intelligence (AI), Retrieval-augmented Generation (RAG), RAG Pipelines, Neo4j, GraphRAG, Agentic AI Systems, LLM Integration, Debugging, LangGraph, Amazon Web Services (AWS)
Machine Learning Engineer
2018 - 2021
Aimbot studio
- Built a real-time sports video analytics platform using deep learning and computer vision, integrating object detection, multi-object tracking, and classification models to deliver accurate, frame-level insights from live video feeds.
- Applied and fine-tuned machine learning and deep learning models for classification, detection, segmentation, and custom ranking across medical, sports, audio, and geophysical datasets.
- Evaluated and customized state-of-the-art approaches, then deployed production-ready AI solutions across multiple domains, improving reliability and enabling real-world business adoption.
Technologies: Python, Computer Vision, Machine Learning, Deep Learning, PostgreSQL, Django, Artificial Intelligence (AI), Debugging, Amazon Web Services (AWS)
Experience
Large-scale Retrieval Augmented Generation (RAG) System
http://cliquify.meI developed an advanced a retrieval augmented generation (RAG) system, leveraging OpenAI and open-source large language models (LLMs). This innovative system is designed to assist recruiters from leading brands by providing real-time, data-driven answers to their queries.
The RAG system integrates seamlessly with existing data sources, enhancing decision-making and efficiency in the recruitment process. This project showcases cutting-edge AI technology applied to real-world challenges in the recruitment industry.
The RAG system integrates seamlessly with existing data sources, enhancing decision-making and efficiency in the recruitment process. This project showcases cutting-edge AI technology applied to real-world challenges in the recruitment industry.
Automated Crack Detection and Report Generation
The PAVE project is a groundbreaking initiative designed to transform highway maintenance in the United States. This innovative venture focuses on identifying cracks and other forms of distress on highways using advanced road imagery captured through highly sensitive cameras. By automating detection, PAVE aims to optimize the extensive resources annually allocated to road surveys and damage assessments, enhancing efficiency and reducing costs associated with manual inspections.
AI-powered WhatsApp Facility Management Assistant
https://al-ghurair.com/en/al-ghurair-property-managementDeveloped and deployed a production-grade WhatsApp assistant for a facility management business, enabling customers to book, reschedule, cancel, and track service requests directly through WhatsApp.
The solution uses Python, FastAPI, LLM-based agents, Redis session management, and back-end API integrations to guide users through service categories, property selection, issue details, scheduling, and booking confirmation. It also supports structured workflows, validation, error handling, and real-time communication with enterprise facility management systems.
The microservices were containerized with Docker and deployed on Azure Container Apps, with secure environment configuration, logging, scalability, and production monitoring. The assistant reduced manual customer-service effort and provided customers with a faster and more convenient way to manage facility maintenance requests.
The solution uses Python, FastAPI, LLM-based agents, Redis session management, and back-end API integrations to guide users through service categories, property selection, issue details, scheduling, and booking confirmation. It also supports structured workflows, validation, error handling, and real-time communication with enterprise facility management systems.
The microservices were containerized with Docker and deployed on Azure Container Apps, with secure environment configuration, logging, scalability, and production monitoring. The assistant reduced manual customer-service effort and provided customers with a faster and more convenient way to manage facility maintenance requests.
Education
2016 - 2019
Master's Degree in Computer Science
Information Technology University of the Punjab - Lahore, Punjab, Pakistan
2012 - 2016
Bachelor's Degree in Information Technology
Punjab University College of Information Technology (PUCIT) - Lahore, Punjab, Pakistan
Skills
Libraries/APIs
REST APIs, PyTorch, Keras, TensorFlow, OpenCV, WhatsApp API
Tools
Azure Machine Learning, GraphRAG
Languages
Python
Frameworks
LangGraph, Agentic Frameworks, Django
Paradigms
Model Context Protocol (MCP)
Platforms
Amazon Web Services (AWS)
Storage
PostgreSQL, Neo4j, Databases
Other
RAG Systems, FastAPI, Generative Artificial Intelligence (GenAI), LangChain, Vector Databases, MLflow, Artificial Intelligence (AI), Machine Learning Operations (MLOps), Retrieval-augmented Generation (RAG), RAG Pipelines, Agentic AI Systems, LLM Integration, Orchestration, AI Agents, Debugging, Machine Learning, Large Language Models (LLMs), Computer Vision, Open-source LLMs, AI Chatbots, Deep Learning, Data Scientist, Agentic AI, ReAct Agents, Vector Data, WhatsApp, Conversational AI, Webhooks, AWS Bedrock AgentCore, AWS ECS Fargate
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