Muhammad Saad Umer

PROFESSIONAL PROFILE I am an AI Engineer and Researcher dedicated to bridging the gap between theoretical Deep Learning and production-grade agentic systems. With a foundation in research from NUST and a deep specialization in Large Language Models (LLMs), my work focuses on building "thinking" systems that go beyond simple API wrappers. My expertise lies in architecting high-performance AI systems using a modern stack of FastAPI, React, and Vector Databases . I specialize in developing advanced Retrieval-Augmented Generation (RAG) pipelines that utilize hybrid search and re-ranking to solve the "hallucination" problem in document-heavy environments. Recently, I have focused on: Model Specialization: Fine-tuning reasoning models like DeepSeek-R1 using Unsloth and QLoRA to align LLMs with specialized domains, such as Cognitive Behavioral Therapy (CBT). Agentic Workflows: Designing autonomous agents with LangChain and n8n that can reason through multi-step tasks, utilize external tools, and self-correct. Production Deployment: Building asynchronous backends capable of handling low-latency AI inference and streaming responses for seamless user experiences. I am driven by the challenge of creating AI that is verifiable, scalable, and capable of autonomous reasoning. I am currently seeking opportunities to apply my skills in RAG architecture and model fine-tuning to build the next generation of intelligent applications. EDUCATION B.E. Software Engineering School of Electrical Engineering and Computer Science , Islamabad , 3.24 (2026) INTERNSHIP EXPERIENCE NUST (College of Aeronautical Engineering) 01-Jun-2024 - 14-Aug-2024 Project: High-Performance Multi-Stream Surveillance System Developed a 2,000+ line production-grade codebase for a surveillance system capable of processing 20+ simultaneous camera streams. Optimized the system to achieve sub-second mean latency across all streams, ensuring real-time operational capability. Authored and published research findings in IEEE ICoDT2 2025 based on the architectural innovations of this system. Engineered a local, noisy audio transcription application using PyQt5 and OpenAI Whisper, optimized for low-resource hardware deployment. Machine Vision and Intelligent Systems Lab 19-Sep-2024 - 01-Jun-2025 Specialization: LLMs in Cheminformatics Conducted advanced research into the intersection of Large Language Models and chemical data processing. Published a First Author review paper in a high-impact factor (IF: 12) journal, establishing a comprehensive framework for LLM applications in Cheminformatics. Collaborated on the development of intelligent vision systems, focusing on the transition from traditional CV to modern transformer-based architectures. National Aerospace Science & Technology Park (NASTP) Simulator Division 01-Jul-2025 - 01-Aug-2025

I am an AI Engineer and Researcher dedicated to bridging the gap between theoretical Deep Learning and production-grade agentic systems. With a foundation in research from NUST and a deep specialization in Large Language Models (LLMs), my work focuses on building "thinking" systems that go beyond simple API wrappers. My expertise lies in architecting high-performance AI systems using a modern stack of FastAPI, React, and Vector Databases . I specialize in developing advanced Retrieval-Augmented Generation (RAG) pipelines that utilize hybrid search and re-ranking to solve the "hallucination" problem in document-heavy environments. Recently, I have focused on: Model Specialization: Fine-tuning reasoning models like DeepSeek-R1 using Unsloth and QLoRA to align LLMs with specialized domains, such as Cognitive Behavioral Therapy (CBT). Agentic Workflows: Designing autonomous agents with LangChain and n8n that can reason through multi-step tasks, utilize external tools, and self-correct. Production Deployment: Building asynchronous backends capable of handling low-latency AI inference and streaming responses for seamless user experiences. I am driven by the challenge of creating AI that is verifiable, scalable, and capable of autonomous reasoning. I am currently seeking opportunities to apply my skills in RAG architecture and model fine-tuning to build the next generation of intelligent applications.