Syed is an AI Research Associate at equitably.ai, a PhD candidate and AI engineer with a specialisation in leveraging innovative AI techniques to drive impact in marketing.
Syed brings deep technical knowledge combined with significant hands-on experience, particularly in deploying LLM-based solutions in production environments. He has served as a Research Assistant at equitably.ai, where he engineered an AI Co-pilot for B2B email marketing. This project showed great practical applications in marketing, leveraging LLMs, HPC, RAG implementation, and fine-tuning to achieve improvements in key metrics such as email response rate from 0.5% to 4.0% and reducing customisation time in current processes by around 200 times.
His AI engineering expertise extends to processing massive datasets, including his time as an AI Engineer, where he engineered a system that processed over 1 TB of media data (3,000+ film clips) and used diverse LLM techniques to automate reel scheduling, a system which experts rated 4 out of 5 for efficiency. He has also worked on creating novel machine learning models using multimodal data for prediction from over 5 TB of video data.
Syed is highly proficient in the core technical stack of modern AI, including Natural Language Processing (NLP), Fine-Tuning, Prompt Engineering, RAG, and Transformer Models. His academic contributions underscore his depth, including creating an LLM-based framework for harmful content detection and scoring (accepted and proceeding to ACM WebConf 2025) and utilising LLMs to discover new heuristics for the 2D Bin-Packing Problem.
Syed’s current academic work as a PhD candidate at City St George’s, University of London, is focused on exploring and improving Large Language Models (LLMs) and Retrieval Augmented Generation (RAG). He holds an MSc in Data Science with Distinction and was awarded an Information Science Scholarship from City St George’s, University of London. He earned his BSc in Computer Science (Distinction) from Brac University.