Nashid Noor Mohammed

WORK HISTORY

EDUCATION

ABOUT NASHID NOOR MOHAMMED

I’m an MSc Artificial Intelligence graduate from the University of Surrey, passionate about designing intelligent systems that combine research innovation with real-world scalability. My expertise spans machine learning, NLP, computer vision, and generative AI, with a focus on large language models (LLMs), fine-tuning, and efficient model orchestration.During my MSc, I developed a collaborative LLM framework (Mixture-of-Agents) that explored how multi-model cooperation can improve response quality and latency, building a strong foundation in inference optimization, orchestration, and AI system design.I also gained experience with vision–language model tuning, LoRA/QLoRA fine-tuning, and building end-to-end AI prototypes using Python, PyTorch, FastAPI, and Docker.I’m currently collaborating on a research project to develop a lightweight foundation model for predicting the Remaining Useful Life (RUL) of rotating machines, combining sequence modeling, representation learning, and efficient architecture design for real-time fault prediction and maintenance optimization.Earlier in my career, I worked at Wipro as a Salesforce Industries (Vlocity) Developer, where I led the implementation of Enterprise Product Catalog (EPC) solutions for telecom and digital service clients. I designed complex product hierarchies, pricing, and eligibility rules using Vlocity’s declarative tools, Einstein Prediction Builder, and custom Apex logic to streamline product configuration and improve delivery efficiency. I also optimized Integration Procedures and DataRaptors for high-performance data orchestration between systems and provided technical leadership through defect triage, client demos, and production rollouts. This experience strengthened my foundation in enterprise software development, process automation, and large-scale system design within fast-paced, client-facing environments.I’m particularly interested in roles involving AI, ML, NLP, CV, or GenAI engineering, where I can apply both my research expertise and engineering background to build scalable, production-ready AI systems.