About the Business Quilter plc is a leading wealth management business, helping to enable brighter financial futures for every generation. Quilter oversees £126.3 billion in customer investments (as of August 2025). It has an adviser and customer offering spanning financial advice, investment platforms, multi-asset investment solutions, and discretionary fund management. The business is comprised of two segments : Affluent and High Net Worth. Affluent encompasses the financial planning business, Quilter Financial Planning, the Quilter Investment Platform and Quilter Investors, the multi-asset investment solutions business. High Net Worth includes the discretionary fund management business, Quilter Cheviot, together with Quilter Cheviot Financial Planning – offering a highly personalised service to private clients, charities, trustees, and professional partners. Quilter Cheviot has presence throughout the UK, Ireland and Channel Islands. At Quilter we never stand still. Our foundations are rooted in our extraordinary expertise, which is trusted by hundreds of thousands of customers, but we have great ambitions to stay one step ahead and make an even greater difference to the people and communities we serve, including our colleagues. Our business is transforming, continually modernising, and becoming even more customer centric. So, if you want to be bold in the pursuit of your ambitions, bring new ideas, and challenge and evolve what we do, it's the perfect time to join us!# About the Role Level : 5Department : COOLocation : Southampton or LondonContract type : PermanentThe new role sits within Chief Operating Office (COO) and reports Head of AI & Data Science. The key accountabilities for the role are as follows : AI / ML Solution Delivery : Hands on end-to-end development and deployment of both traditional and GenAI-based machine learning models, including discovery analytics, experimental design, model development, benchmarking, enhancement and deployment. LLM & RAG Integration : Design and implement new Retrieval-Augmented Generation (RAG) pipelines and enhance existing frameworks, applying advanced techniques in data chunking / splitting, vectorization, knowledge graph representation (GraphRAG), and query retrieval and evaluation. Model Evaluation & Prompt Engineering : Design and execute experiments to benchmark and evaluate model performance using both classical metrics (precision, recall, F-score) and GenAI-specific techniques (LLM-as-a-Judge, ROUGE, GANs). Develop and refine prompts to optimize GenAI model reasoning, accuracy, and overall effectiveness. Responsible AI : Apply strong understanding of ethical AI principles and governance frameworks, ensuring robust model validation and compliance with regulatory and organizational standards. Cross-Functional Collaboration : Work closely with business partners, stakeholders, and technical teams (data engineering, platform engineering) to translate business requirements into impactful AI solutions. Research & Innovation : Stay abreast of emerging tools, techniques, and best practices in LLMs, RAG, GenAI, model development & evaluation techniques and proactively apply new knowledge to drive innovation.# About You Knowledge Strong foundation in core machine learning algorithms and deep learning concepts (e.g., Neural Nets, CNNs, GANs, RNNs, Transformers). In-depth understanding of natural language processing (NLP) model development and large language model (LLM) fine-tuning, evaluation and RAG integration ( Eg : BERT, GPT, Claude, LLaMA, Mistral) Deep understanding of prompt engineering techniques, including zero-shot and few-shot prompting strategies with ability to enhance the performance of LLMs and agentic AI systems. Solid grasp of data structures and handling for both structured (SQL, pandas) and unstructured (text, images, audio) data, including knowledge bases creation with Graph (Neo4j) and vector stores. Awareness of AI governance, and regulatory considerations, especially in financial services. Experience Proven track record in building, fine-tuning, and evaluating traditional ML, NLP and LLM-based models. Hands-on experience with leading ML frameworks (e.g., PyTorch, TensorFlow) and LLM libraries (e.g., Hugging Face, LangChain / LangGraph, LlamaIndex). Experience in RAG and / or GraphRAG pipeline development and evaluation combining with chunking, vectorization (vector store and / or knowledge graph) and enhanced retriever-reranker techniques.
J-18808-Ljbffr
Senior Scientist • Holiday, FL, US