AI Strategy & Leadership: Lead the end-to-end development of advanced AI models and frameworks (including large language models, transformers, and agent-based systems), aligning AI initiatives with business goals. Provide technical leadership to data science and engineering teams, and set strategic direction for AI projects.

Architectural Design: Design and oversee the integration of AI components such as LLMs, transformer architectures, retrieval-augmented generation (RAG) workflows, and vector databases into scalable solutions. Establish standards for model architecture and pipeline optimisation to ensure robustness and efficiency.

Evaluation & Safety: Define and enforce rigorous evaluation protocols for AI models, including performance metrics, validation techniques, and safety checks. Ensure all AI systems meet reliability standards, ethical guidelines, and regulatory requirements for responsible AI usage.

MLOps & Deployment: Guide the development of MLOps pipelines for continuous training, testing, and deployment of models in cloud environments. Oversee infrastructure decisions (without bias to specific platforms) to guarantee that AI services are scalable, secure, and maintainable in production.

Educational Background: Master's or PhD in Computer Science, Artificial Intelligence, Machine Learning or a related field. Strong theoretical foundation in machine learning, deep learning, and AI system design.

Technical Expertise: Expert-level proficiency in programming (e.g. Python) and ML frameworks (such as TensorFlow or PyTorch). In-depth knowledge of modern AI techniques including natural language processing, transformer networks, reinforcement learning, and data engineering for AI (ETL, feature engineering).

AI/ML Tools: Extensive experience with AI and MLOps toolchains

from model development and version control to continuous integration and automated deployment. Familiarity with vector stores/databases, orchestration of agent frameworks, and evaluation libraries/methodologies for model performance.

Analytical Skills: Exceptional problem-solving abilities and mathematical skills (linear algebra, calculus, statistics) to innovate and troubleshoot complex AI modelling challenges.

Leadership & Strategic Thinking: Strong leadership qualities with the ability to mentor team members, make strategic decisions for AI initiatives, and communicate a clear vision for AI adoption across the organisation.

Communication: Excellent communication skills, capable of conveying complex AI concepts to both technical colleagues and non-technical stakeholders in a clear and concise manner.

Collaboration: A collaborative mindset, encouraging knowledge sharing and effective teamwork across different departments and disciplines.

Problem-Solving & Adaptability: Innovative and analytical thinker who approaches challenges methodically. Adaptable to rapidly evolving AI technologies and industry trends, continuously learning and updating skills to drive innovation.