Job Specifications
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 : 5
Department: COO
Location : Southampton or London
Contract type : Permanent
The new role sits within Group Chief Operation Office and reports to Head of AI and Data Science. The key accountabilities for the role are as follows:
AI/ML Solution Development : Design and implement advanced AI solutions spanning traditional machine learning, Generative AI, and Agentic AI paradigms. This includes leading hands-on development of models and RAG pipelines to support enterprise-grade LLM applications.
MLOps and LLMOps : Build and maintain scalable MLOps and LLMOps pipelines, enabling automation of model training, testing, deployment, and monitoring. Enable reproducibility, traceability, and performance optimization across all AI solutions.
Vendor Product Evaluation & Onboarding : Lead the technical evaluation of third-party AI products and platforms, conducting benchmarking, sandbox testing, and assessing integration feasibility. Collaborate with procurement, legal, and security teams to ensure successful onboarding of approved vendors.
Governance & Compliance : Define and enforce standards for model validation, bias detection, and explainability, ensuring alignment with internal governance frameworks and external regulations such as GDPR and FCA
Platform Enablement: Leverage Databricks, Azure AI, and Microsoft Copilot to accelerate solution delivery, while developing reusable AI components, APIs, and Copilot plugins to support business teams and drive adoption.
About You
Knowledge
Strong understanding of machine learning engineering principles, including pipeline design, model lifecycle management, and production deployment including CI/CD, model registries, and automated workflows while managing feature stores and internal knowledge bases for RAG.
Strong grasp of prompt engineering techniques, including zero-shot and few-shot prompting strategies with ability to enhance the performance of LLMs and agentic AI systems.
Familiarity of LLM model evaluation techniques such as ROUGE, LLM-as-a-Judge and BERT.
Deep knowledge of cloud-native AI platforms and tools, particularly Databricks, Azure, AWS, Docker.
Awareness of regulatory and compliance considerations in AI, especially within financial services and other regulated industries.
Experience
Hands-on experience with leading ML frameworks (e.g., PyTorch, TensorFlow) and LLM libraries (e.g., Hugging Face, LangChain/LangGraph, LlamaIndex).
Practical experience implementing CI/CD pipelines using tools like GitHub Actions or Jenkins, and managing MLOps and LLMOps with MosiacAI, MLflow, Sagemaker or similar platforms.
Design and develop lightweight, interactive user interfaces using frameworks such as Streamlit, Flask, or similar tools to support stakeholder and client demonstrations.
Led technical due diligence and integration planning for onboarding external AI platforms or tools.
Background in financial services or regulated industries is a strong advantage.
Skills
Advanced programming skills in Python (and optionally, other languages such as R or Java).
Ability to design and implement scalable ML, LLM pipelines and infrastructure.
Skilled in benchmarking, sandbox testing, and integration of vendor AI products.
Strong analytical and problem-solving skills, with attention to performance optimization
Effective communication and collaboration skills for cross-functional engagement with engineering, procurement, legal, and governance teams.
Qualifications
PhD/MSc