Glasgow City Center, hybrid
Artificial Intelligence \& Data Science
Lead AI Scientist
DO NOT APPLY IF YOU REQUIRE SPONSORSHIP NOW OR AFTER YOUR GRADUATE VISA PLEASE..
FD Intelligence (FDI) is at the forefront of AI-driven automation, delivering advanced solutions that transform how accountancy, finance, and professional services firms operate. Our mission is to blend mathematical rigour with practical AI innovation, enabling businesses to work smarter, faster, and with greater accuracy.
As an AI Graduate Scientist at FD Intelligence, you will design, develop, and deploy cutting-edge AI solutions that push the boundaries of automation and document intelligence. You’ll work alongside a multidisciplinary team of mathematicians, engineers, and domain experts, applying state-of-the-art techniques in natural language processing (NLP), computer vision, and machine learning to solve complex, real-world problems.
This role is ideal for someone with a strong mathematical background, a passion for AI research, and a drive to deliver tangible business impact through innovative technology.
: Investigate and implement advanced AI/ML algorithms, focusing on document intelligence, information retrieval, and data processing automation.
: Contribute to the development of FDI’s flagship document intelligence platform, leveraging NLP and computer vision to extract, classify, and structure data from complex, unstructured forms.
: Architect and implement intelligent retrieval systems, exploring advanced techniques such as GraphRAG and Google ScaNN for improved precision and contextual relevance.
: Build, test, and deploy AI-powered bots and web applications on Microsoft Azure, ensuring scalability, security, and performance.
: Develop Model Context Protocol (MCP) systems to seamlessly integrate AI models with enterprise data sources, enabling domain-specific AI interactions.
: Research and apply model interpretability techniques to make complex AI systems transparent, reliable, and business-ready.
: Stay abreast of cutting-edge AI research, regularly evaluating and implementing promising new methods.
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