Senior AI​/ML Scientist

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: 4

Department: COO

Location:

Southampton or London Contract type:
Permanent The new role sits within AI Centre of Excellence department under Chief Operating Office (COO). 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, BERTScore). Develop and refine prompts to optimise GenAI model reasoning, accuracy, and overall effectiveness.

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 Qualifications Advanced  degree (MSc or PhD) in Machine Learning, Natural Language Processing, Artificial Intelligence, or a related field.

Proven track record of delivering AI solutions from research to production in real-world applications.

Knowledge Strong foundation in machine learning algorithms and deep learning concepts including Neural Networks and Transformer-based architectures.

Knowledge in developing and deploying scalable models on Databricks, Azure, and AWS, leveraging tools such as FastAPI and Docker.

Proficient in model tracing and observability for LLM in production and implementing evaluation frameworks for model quality and reliability.

Strong understanding of software engineering best practices, including version control, testing, and CI/CD for production-ready AI systems.

Domain expertise in financial services or other regulated industries is highly desirable.

Experience Strong experience in AI/ML research and development, specializing in deep learning-based NLP, Information Retrieval, and Generative AI.Proven expertise in RAG/GraphRAG pipeline development and evaluation, including advanced retriever-reranker techniques.

Experience in building knowledge bases and ontologies is highly desirable.

Extensive experience in defining and implementing evaluation metrics for GenAI systems such as Recall, Precision, NDCG, LLM-as-a-Judge, BERTScore, BLEU score, and hallucination detection.

Provide mentorship and technical guidance to junior AI Scientists Skills Strong  proficiency in Python and experience with frameworks such as Num Py, Pandas, Scikit-learn, and modern Generative AI libraries (e.g., Lang Chain, Llama Index, Azure AI Foundry).Hands-on experience with PyTorch, and leading LLM libraries such as Hugging Face, Lang Chain, Lang Graph, and Llama Index.

Skilled in hypothesis formulation, defining evaluation metrics, conducting literature reviews, and building reproducible prototypes with critical outcome analysis.

Rapid Experimentation…