• Design and execute advanced AI/ML models – including fine-tuned LLMs and RAG architectures – for integration into both internal tooling and external client-facing products. • Lead experimentation efforts: clearly define hypotheses, design interpretable tests, analyze results rigorously, and communicate findings effectively. • Serve as the technical SME for GenAI/LLM-powered tools, from foundational architecture to model optimization and deployment. • Work with software and data engineering to productionize models and contribute to robust CI/CD pipelines with MLOps/LLMOps best practices. • Stay current on emerging methods in AI – e.g., agentic workflows, memory-augmented systems, multimodal embeddings – and bring those ideas to life in real-world applications. • Promote AI adoption internally by educating and empowering cross-functional partners while improving processes and workflows. • Manage your time and projects effectively, ensuring clear progress updates and proactive communication with collaborators. • Write production-grade Python code to support models, pipelines, and experimental frameworks. • Work with the highest degree of ethics and empathy, recognizing that AI can be a potentially disruptive force for organizations and individuals.

• Design and execute advanced AI/ML models – including fine-tuned LLMs and RAG architectures – for integration into both internal tooling and external client-facing products. • Lead experimentation efforts: clearly define hypotheses, design interpretable tests, analyze results rigorously, and communicate findings effectively. • Serve as the technical SME for GenAI/LLM-powered tools, from foundational architecture to model optimization and deployment. • Work with software and data engineering to productionize models and contribute to robust CI/CD pipelines with MLOps/LLMOps best practices. • Stay current on emerging methods in AI – e.g., agentic workflows, memory-augmented systems, multimodal embeddings – and bring those ideas to life in real-world applications. • Promote AI adoption internally by educating and empowering cross-functional partners while improving processes and workflows. • Manage your time and projects effectively, ensuring clear progress updates and proactive communication with collaborators. • Write production-grade Python code to support models, pipelines, and experimental frameworks. • Work with the highest degree of ethics and empathy, recognizing that AI can be a potentially disruptive force for organizations and individuals.

• Design and execute advanced AI/ML models – including fine-tuned LLMs and RAG architectures – for integration into both internal tooling and external client-facing products. • Lead experimentation efforts: clearly define hypotheses, design interpretable tests, analyze results rigorously, and communicate findings effectively. • Serve as the technical SME for GenAI/LLM-powered tools, from foundational architecture to model optimization and deployment. • Work with software and data engineering to productionize models and contribute to robust CI/CD pipelines with MLOps/LLMOps best practices. • Stay current on emerging methods in AI – e.g., agentic workflows, memory-augmented systems, multimodal embeddings – and bring those ideas to life in real-world applications. • Promote AI adoption internally by educating and empowering cross-functional partners while improving processes and workflows. • Manage your time and projects effectively, ensuring clear progress updates and proactive communication with collaborators. • Write production-grade Python code to support models, pipelines, and experimental frameworks. • Work with the highest degree of ethics and empathy, recognizing that AI can be a potentially disruptive force for organizations and individuals.

• Lead three AI Ops Engineers — own their delivery, development, and day-to-day work • Prioritise efficiently — manage competing requests from multiple teams by applying a clear prioritisation framework you'll help develop and improve • Design and ship multi-agent systems and production-grade automations that measurably improve how internal teams and customer-facing staff work • Build RAG pipelines for internal knowledge retrieval that ground agents in real organisational context • Keep the AI tooling stack healthy as usage scales across 2,500+ employees • Stay current on emerging AI tools and technologies - know what's hype and what's actually useful • Help refine AI training for the wider organisation, including Software Engineering teams