Lead Applied Scientist – Generative AI & Agentic Systems

Hybrid – minimum 3 days per week in the local office

£90,000–£120,000 base + 15–20% target bonus

We are looking for an exceptional Lead Applied Scientist to join the Data Science & AI function of a major international organisation investing heavily in Generative AI, Large Language Models and Agentic AI.

This is a senior, hands-on position for an accomplished Applied Scientist who combines deep expertise in modern AI with strong mathematical and statistical foundations.

You’ll act as a scientific authority for AI, setting the standards for how solutions are designed, evaluated, validated and monitored. You’ll work on some of the organisation’s most challenging AI problems while providing technical leadership and mentorship to Data Scientists and AI Engineers.

The focus is increasingly on LLMs and agentic systems, including RAG, tool use and multi-step agent workflows. However, this role is fundamentally about selecting the right scientific approach for each problem, drawing on machine learning, deep learning, statistics and optimisation where appropriate.

This is not a purely advisory or leadership position. You’ll remain highly hands-on, building models and AI applications, writing Python, designing experiments, reviewing code and solving technically challenging problems.

Responsibilities

• Leading the scientific design and development of AI and machine learning solutions, translating complex business problems into measurable technical objectives.
• Designing sophisticated Generative AI and Agentic AI systems, including prompting, RAG, tool use and multi-step agent workflows.
• Defining how LLM and agentic solutions are evaluated, including metrics, test sets, benchmarks, acceptance thresholds and evaluation frameworks.
• Applying mathematical and statistical rigour to experimentation, uncertainty, error analysis and solution validation.
• Acting as the technical escalation point for particularly complex or ambiguous AI problems.
• Holding scientific ownership for solution quality and ensuring approaches are robust before deployment.
• Solving challenging problems involving unstructured documents, expert workflow automation, forecasting, optimisation, portfolio analytics and claims analytics.
• Selecting the appropriate approach for each use case, whether that involves LLMs, agentic AI, deep learning, traditional machine learning or statistical methods.
• Building production-quality solutions directly, particularly for novel, technically challenging or higher-risk projects.
• Developing approaches to improve the accuracy, reliability and robustness of LLM and agent outputs.
• Defining monitoring approaches to identify model degradation and drift once solutions reach production.
• Leading Responsible AI practices, including bias and fairness testing, explainability and validation of model and agent behaviour.
• Setting standards for experimentation, evaluation, coding and technical documentation.
• Reviewing code, experiments and technical outputs produced across the wider team.
• Mentoring and developing Data Scientists and AI Engineers through pairing, technical reviews and knowledge sharing.
• Working closely with engineering leadership on architecture, deployment and productionisation.
• Communicating technical methods, results, limitations and trade-offs clearly to senior business stakeholders and governance teams.