Muhammad Usman S.

Stop paying for AI prototypes that break in production. If you need enterprise-grade AI agents, robust RAG systems, or deterministic LLM infrastructure, you need an engineer, not just a prompt writer. I'm Usman, a Full-Stack AI Engineer specializing in agentic workflows, retrieval-augmented generation (RAG), and the MLOps infrastructure required to keep it all running at scale. I don't build demos; I architect production-ready AI ecosystems that run 24/7 in real business environments across healthcare, SaaS, distribution, and enterprise sectors. Core Capabilities & Architecture Multi-Agent Ecosystems: I build autonomous systems using LangChain, LangGraph, CrewAI, and Claude. From dynamic routing to structured outputs and tool use, I engineer agents that can reason through complex tasks and execute flawlessly. Enterprise RAG Pipelines: End-to-end semantic retrieval systems built for millisecond latency. I implement hybrid search, semantic chunking, and reranking across vector databases (Pinecone, Qdrant, ChromaDB) to ensure your AI only outputs verified data. Self-Hosted LLM Infrastructure: Want to cut managed provider costs without sacrificing speed? I deploy vLLM and TGI on RunPod with OpenAI-compatible endpoints for high-throughput, secure, local inference. Autonomous Voice Agents: Zero-latency voice systems utilizing ElevenLabs, Whisper, and Retell AI that handle complex scheduling, support, and inquiries with zero human input. Workflow Automation & MLOps: I connect custom Python backends to n8n and Make, supported by enterprise-grade deployment pipelines using Docker, Kubernetes, AWS, and GCP. 🛠️ The Tech Stack AI/LLMs: OpenAI API, Claude, Gemini, LLaMA 3, Mistral, RunPod, vLLM, MCP Servers Frameworks: LangChain, LangGraph, LlamaIndex, CrewAI, AutoGen Databases: Pinecone, Qdrant, ChromaDB, FAISS, PostgreSQL, Redis Infrastructure & Tools: AWS, GCP, Azure, Docker, Kubernetes, FastAPI, Python, LangFuse, LangSmith ⚡ The Engineering Advantage Production-First: Everything I ship is containerized, monitored, documented, and built to scale. Precision Debugging: Routing, retrieval, prompting, and infrastructure failures are four different problems. I debug at the correct layer using LangFuse/LangSmith trace-level observability. No Surprises: Clear communication, deterministic outcomes, and strict adherence to scope. If your business needs AI architecture that actually holds up in the real world, let's talk about what that looks like for your use case.