Muhammad Usman B.

Most AI projects never reach real users. They look great in a demo, get approved, and then break as soon as real data and real customers show up. The problem is rarely the model. It’s the engineering around it. 𝗧𝗵𝗮𝘁‘𝘀 𝘄𝗵𝗲𝗿𝗲 𝗜‘𝗺 𝗱𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝘁. I’m an AI Engineer with 9+ years of experience in the industry and 4+ years building voice agents, AI agents, automation systems, and full-stack AI products that hold up in daily use. My longest engagement ran three years as tech lead with 2,700+ hours, and one startup re-hired me the same week a project phase ended. As an AI Engineer, every system I build comes with error handling, logging, recovery steps, and a clear plan for what happens when something goes wrong. That’s what turns a demo into a product people can rely on. 𝗪𝗵𝗮𝘁 𝗜 𝗕𝘂𝗶𝗹𝗱 𝗩𝗼𝗶𝗰𝗲 𝗔𝗜 & 𝗧𝗲𝗹𝗲𝗽𝗵𝗼𝗻𝘆 Inbound and outbound voice agents that answer calls, book appointments, qualify leads, and update your CRM in real time. Speech-to-text, natural conversation flow, text-to-speech, and smooth handoff to a human when the caller needs one, with a backup plan if that transfer fails. 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁𝘀 & 𝗠𝘂𝗹𝘁𝗶-𝗔𝗴𝗲𝗻𝘁 𝗪𝗼𝗿𝗸𝗳𝗹𝗼𝘄𝘀 AI agents that take over repetitive work, with human approval on important decisions and a record of every action. For example, I built LexGen for an Australian law firm, a set of AI agents that handle document processing, review, client intake, routing, and follow-ups. 𝗥𝗔𝗚 & 𝗔𝗜 𝗦𝘂𝗽𝗽𝗼𝗿𝘁 𝗔𝗴𝗲𝗻𝘁𝘀 Chatbots and assistants that answer questions from your own documents and data, with sources behind every answer. For UpFix, I built an AI support agent that collects product details and guides customers through refunds and replacements using the company’s own data. 𝗙𝗶𝘅𝗶𝗻𝗴 𝗔𝗜 𝗦𝘆𝘀𝘁𝗲𝗺𝘀 𝗧𝗵𝗮𝘁 𝗕𝗿𝗲𝗮𝗸 Many clients come to me with AI that worked in testing but failed in real use. Largo was one of them. I took over their system, fixed the weak points, and added the monitoring and testing it was missing so the business could depend on it. 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴, 𝗡𝗟𝗣 & 𝗖𝗼𝗺𝗽𝘂𝘁𝗲𝗿 𝗩𝗶𝘀𝗶𝗼𝗻 Custom models built and deployed in Python and TensorFlow, from text analysis and document understanding to image recognition and predictions. 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻 Workflows in n8n and Make that connect your CRM, email, and internal tools so manual tasks run on their own. 𝗙𝘂𝗹𝗹-𝗦𝘁𝗮𝗰𝗸 𝗔𝗜 𝗣𝗿𝗼𝗱𝘂𝗰𝘁𝘀 Web apps with user accounts, role-based access, dashboards, and AI features built in. Next.js on the front end, Python on the back end, deployed on the cloud. 𝗪𝗵𝗼 𝗜 𝗪𝗼𝗿𝗸 𝗪𝗶𝘁𝗵 Healthcare, fintech, legal, e-commerce, real estate, and growing businesses of all kinds. Founders and teams who want an engineer who will tell them the right approach before building anything. Zory came to me with only an idea, and we spent weeks shaping it before I designed the AI solution. I also document everything, so you own the system after handover, not just the code. 𝗦𝘁𝗮𝗰𝗸 & 𝗧𝗼𝗼𝗹𝘀 Voice AI: telephony, speech-to-text, text-to-speech, conversation orchestration  AI & LLMs: OpenAI API, ChatGPT, Anthropic Claude, vector databases, tool calling  Agent frameworks: LangChain, LangGraph, CrewAI  Machine learning: TensorFlow, Python  Full-stack: Next.js, React, Django, FastAPI  Automation: n8n, Make, API integrations  Infrastructure: Docker, AWS, GCP, Azure If you’re building a voice agent, an AI agent, an automation, or an AI product and want an honest opinion on the best way to do it, send me the details and I’ll tell you exactly how I’d approach it. 𝗞𝗲𝘆𝘄𝗼𝗿𝗱𝘀 AI Engineer · Artificial Intelligence · Python · Data Science · Large Language Model · Generative AI · Machine Learning · Natural Language Processing · Computer Vision · Deep Learning · TensorFlow · Automation · LangChain · AI Agent Development · AI Model Development · n8n · Make · OpenAI API · ChatGPT · API Integration · Next.js · Voice AI