Applied Scientist

i3; Greater London; Hybrid; GBP 120,000 - 165,000; Full time

Job summary

i3 in London is seeking a senior Applied Scientist to own and deliver AI solutions focused on LLMs, Generative AI and agentic systems within insurance data. You will write production-quality Python, design rigorous evaluation frameworks and lead scientific directions from problem framing to deployment.

You will work hands-on with Databricks, MLflow and Spark, mentor colleagues, and ensure robust modelling, validation and responsible AI practices in a hybrid London setting.

Qualifications

• Deep hands-on experience with LLMs and Generative AI, incl. RAG, prompt engineering, fine-tuning, and agentic AI systems.
• Excellent Python skills and strong foundations across statistics and ML.
• Experience building evaluation frameworks for AI systems and production-ready solutions.
• Production experience and familiarity with Databricks, MLflow and Spark.

Responsibilities

• Lead scientific design and development of AI/ML, Generative AI and agentic AI solutions.
• Design LLM applications with RAG, tool use, prompting and multi-step agent workflows.
• Own evaluation methodologies for LLMs, RAG and agentic systems.
• Apply rigorous statistics to experimentation, uncertainty, model behaviour and validation.
• Develop robust AI solutions for insurance data and complex domains.
• Set standards for experimentation, evaluation, coding and documentation.
• Mentor data scientists and engineers through technical reviews.
• Collaborate with Databricks, MLflow and Spark in production environments.

Skills

LLMs

Generative AI

Agentic AI

Python

Statistics

Probability

Machine learning

Experimental design

Model validation

Evaluation frameworks

RAG

Prompt engineering

Fine-tuning

Databricks

MLflow

Spark

Education

Master's degree in CS/Math/Statistics

Tools

Databricks

MLflow

Spark

Job description

Generative AI & Agentic AI | Insurance | London

London - hybrid, 3 days per week in the office

Previous insurance or reinsurance experience is required, ideally with an understanding of underwriting, claims, actuarial or other insurance data.

This is a senior, hands-on position for an experienced Applied Scientist who wants to take scientific ownership of sophisticated AI solutions, with a particular focus on LLMs, Generative AI and agentic systems.

You'll act as a senior technical authority within the Data Science & AI team, setting the scientific approach and quality standards for AI solutions from initial problem framing and experimentation through to evaluation, validation and production monitoring.

Importantly, this isn't a role where you'll move away from the technology. You'll remain hands-on, tackling complex problems, developing models and AI solutions, writing production-quality Python and reviewing the work of other scientists and engineers.

What you'll be doing:

• Leading the scientific design and development of AI/ML, Generative AI and agentic AI solutions
• Designing LLM applications incorporating RAG, tool use, prompting and multi-step agent workflows
• Owning evaluation methodologies for LLMs, RAG and agentic systems, including metrics, test sets and acceptance thresholds
• Applying strong mathematical and statistical rigour to experimentation, uncertainty, model behaviour and validation
• Solving complex problems involving unstructured documents, expert workflow automation, forecasting, optimisation, portfolio analysis and claims
• Setting standards for experimentation, evaluation, coding and documentation
• Leading Responsible AI approaches covering bias, fairness, explainability and model risk
• Writing production-quality Python and contributing directly to higher-risk or novel AI projects
• Working with Databricks, MLflow and Spark alongside modern LLM and AI frameworks
• Mentoring Data Scientists and Engineers through technical reviews, pairing and scientific guidance
• Acting as the escalation point for challenging scientific and modelling decisions

We're looking for:

• You'll need deep, hands-on experience with LLMs and Generative AI, including RAG, prompt engineering, fine-tuning and the design and evaluation of agentic AI systems.
• You'll also need excellent Python skills and strong foundations across statistics, probability, machine learning, experimental design and model validation.
• Crucially, you should have experience developing robust evaluation frameworks for AI systems - particularly measuring the quality and reliability of LLM, RAG and agent outputs - rather than simply building prototypes.
• Experience taking AI solutions from experimentation through to production is essential, alongside practical exposure to technologies such as Databricks, MLflow and Spark.
• Previous insurance or reinsurance experience is required, ideally with an understanding of underwriting, claims, actuarial or other insurance data.
• A postgraduate qualification in Computer Science, Statistics, Mathematics or another quantitative discipline would be advantageous.