Muhammad Nabeel Asad

As an AI Engineer, I developed autonomous multi-agent systems in Python using LangGraph and Gemini to automate complex workflows. I built a low-latency RAG pipeline with Qdrant for real-time AI responses and orchestrated FastAPI microservices using n8n. To ensure data security, I designed a custom MCP server allowing the LLMs safe access to proprietary internal data. Finally, I containerized the entire application stack with Docker for highly scalable, robust, and production-ready deployments.

As an AI Working Student, I built a full-stack RAG application (Vue.js, Flask) to automate the analysis of customer feedback. I used AWS Bedrock (LLaMA) to summarize the data, embedded it using Cohere 3.5, and stored it in a MongoDB vector database. To guarantee high search accuracy, I applied semantic reranking with Cohere Rerank 3.5. This AI-driven solution allowed the company to instantly query and process customer feedback, reducing manual task time from days to just minutes

In my AI Master's program, I dive deep into the mechanics behind machine learning and big data. My coursework covers everything from building Neural Networks to Data Mining and designing Data Warehouses, which gives me a really strong scientific foundation for the hands-on AI engineering work I do.

During my Bachelor's degree in Computer Science, I built a solid foundation in software engineering, algorithms, and database management. The program equipped me with core programming skills and a deep understanding of system architecture, data structures, and the full software development lifecycle. This technical groundwork set the perfect stage for my transition into advanced full-stack development and Artificial Intelligence.