Jahanzeb Riaz

AI Engineer building LLM-powered systems, RAG pipelines, and agentic AI workflows that work in real-world applications.Most AI products fail not because of the model, but because the system around it isn’t designed to handle context, scale, or real data. My focus is on solving this by building AI systems that are reliable, structured, and production-oriented.I work on designing end-to-end AI architectures where LLMs are integrated into applications through retrieval pipelines, agent-based workflows, and scalable backend systems. This includes handling context, enabling multi-step reasoning, and ensuring consistent system behavior under real-world conditions.Using frameworks like LangChain and LangGraph, I build workflows that go beyond single responses and support dynamic decision-making and automation. On the backend side, I develop scalable APIs with FastAPI to integrate AI systems into full applications.I have built multiple AI-driven systems, including conversational RAG applications for document-based Q&A, multi-agent workflows for complex task execution, and full-stack AI applications combining backend intelligence with user interfaces.My work focuses on system-level challenges such as:→ Handling unstructured and evolving data→ Maintaining response consistency→ Designing scalable retrieval and search systems→ Integrating AI into real product environments𝗧𝗲𝗰𝗵 𝗦𝘁𝗮𝗰𝗸𝗔𝗜 / 𝗟𝗟𝗠 𝗦𝘆𝘀𝘁𝗲𝗺𝘀:  • LangChain, LangGraph, LlamaIndex, CrewAI  • LLMs: GPT, Llama, Gemma  • RAG pipelines, multi-agent systems, context-aware workflows𝗕𝗮𝗰𝗸𝗲𝗻𝗱 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴:  • FastAPI  • REST APIs, system design, AI integration𝗗𝗮𝘁𝗮𝗯𝗮𝘀𝗲𝘀 & 𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹:  • FAISS, Pinecone, Chroma  • PostgreSQL, MySQL𝗙𝗿𝗼𝗻𝘁𝗲𝗻𝗱:   • React/Next.js𝗗𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁 & 𝗜𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲:  • Docker, CI/CD pipelines  • Streamlit (for prototyping & demos)I approach AI engineering from a systems perspective—where performance, reliability, and integration matter as much as model capability.Currently focused on growing as an AI Engineer by building scalable GenAI applications, improving backend integration skills, and exploring advanced AI workflows and agent-based systems.