Muhammad T.

𝐈𝐟 𝐲𝐨𝐮'𝐫𝐞 𝐥𝐨𝐨𝐤𝐢𝐧𝐠 𝐟𝐨𝐫 𝐬𝐨𝐦𝐞𝐨𝐧𝐞 𝐭𝐨 𝐤𝐧𝐨𝐜𝐤 𝐨𝐮𝐭 𝐚 𝐬𝐦𝐚𝐥𝐥 𝐭𝐚𝐬𝐤 𝐨𝐧 𝐚 𝐭𝐢𝐠𝐡𝐭 𝐛𝐮𝐝𝐠𝐞𝐭, 𝐈'𝐦 𝐧𝐨𝐭 𝐭𝐡𝐞 𝐫𝐢𝐠𝐡𝐭 𝐟𝐢𝐭. 𝐌𝐲 𝐰𝐨𝐫𝐤 𝐢𝐬 𝐟𝐨𝐫 𝐭𝐞𝐚𝐦𝐬 𝐩𝐮𝐭𝐭𝐢𝐧𝐠 𝐀𝐈 𝐢𝐧𝐭𝐨 𝐩𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐨𝐧 𝐚𝐧𝐝 𝐤𝐞𝐞𝐩𝐢𝐧𝐠 𝐢𝐭 𝐭𝐡𝐞𝐫𝐞. I'm an AI engineer working on AI agents, RAG systems, generative AI, and LLM automation. $100K+ earned on Upwork, 27 projects delivered, Top Rated, with production AI and enterprise software running for clients in healthcare, SaaS, and compliance-heavy industries. Most clients find me after an AI prototype stalled before launch. I build systems that survive real users, real data, and real compliance requirements. 🤖 AI Agent Development Multi-agent systems and autonomous workflows using LangGraph, CrewAI, AutoGen, and custom architectures. Agents that make decisions, call your APIs, process documents, and finish real business tasks end to end. 📚 RAG Systems & Enterprise Document Intelligence Retrieval Augmented Generation pipelines over your documents and knowledge bases: semantic search, chat-with-your-data, enterprise document intelligence. I've built RAG systems searching 100,000+ documents with sub-second latency using Qdrant, Pinecone, ChromaDB, and FAISS, including fully on-premise, air-gapped deployments for compliance-heavy organizations. 🧠 LLM Integration & AI App Development Production integration of OpenAI (GPT-4/GPT-5), Anthropic Claude, Gemini, Llama, and open-source models. This includes hybrid model routing that sends simple tasks to fast, cheap models and complex reasoning to heavier ones, which cuts inference cost without cutting quality. Chatbots, document processing, classification, and AI features inside existing products. ⚙️ AI Automation & Workflows LLM-powered automation with n8n, Make, and Python: lead processing, data extraction, content pipelines, and internal tooling. If your team is copy-pasting between tools, I can probably automate it. ☁️ Cloud Infrastructure & AI Security AWS (EC2, ECS, S3, Bedrock), GCP, Docker, Kubernetes. Scalable deployments, GPU inference, encrypted data pipelines, on-premise and air-gapped LLM deployments, role-based access control, and audit trails that hold up to enterprise review. Recent work: a document intelligence platform with on-premise LLMs and SAP integration, and a multi-agent healthcare SaaS platform aggregating data from 32 medical directories with LLM-powered analysis and content generation. How I work: ✅ Production code with testing, documentation, and monitoring ✅ Fixed-price milestones or clearly scoped hourly engagements ✅ Clear async communication and regular progress demos ✅ Ongoing optimization after deployment. Most clients retain me after the first build. Stack: Python, FastAPI, LangChain, LangGraph, OpenAI API, Claude, Qdrant, Pinecone, PostgreSQL, Supabase, n8n, Make, AWS, GCP, Docker, Kubernetes, Enterprise Software, AI Automation Tell me what you're building or trying to automate. You'll get specific questions about your use case back, not a copy-paste pitch.