Own the overall system architecture, understanding how components interact, where dependencies create risk and where production realities challenge theoretical designs.
Make day-to-day architectural decisions, defining what gets built, how it is structured and the interface contracts between components, while partnering with the Director of AI and Decision Intelligence on major strategic decisions.
Translate research ideas into production-ready system designs, evaluating integration approaches, engineering effort, trade-offs and delivery sequencing.
Identify architectural risks early, ensuring short-term decisions do not create long-term constraints or unnecessary technical debt.
Build prototypes that validate architectural assumptions, integration patterns and scalability requirements, rather than simply proving that a model or idea works in isolation.
Define clear boundaries and interfaces between learned and non-learned system components, enabling both to evolve independently without introducing instability.
Guide technical decision-making across the team, ensuring implementation choices remain aligned with the long-term architecture and product vision, even when delivery pressures favour short-term solutions.
Act as a technical sounding board for complex design and systems challenges, helping the team make pragmatic decisions in areas with significant uncertainty or trade-offs.

Experience operating at Staff Engineer, Principal Engineer or equivalent scope within teams building AI or ML-powered products and systems.
A track record of owning end-to-end system architecture, from design through to production, for complex AI or ML systems operating under real-world technical, product and operational constraints.
Strong software engineering fundamentals. You write clean, maintainable and reviewable code in Python or C#/.NET, and understand why engineering quality matters as systems scale.
Deep understanding of the trade-offs involved in AI system design, including latency versus accuracy, trainability versus interpretability, modularity versus coupling, and engineering pragmatism versus theoretical elegance.
Sufficient ML knowledge to engage credibly in discussions around model behaviour, evaluation approaches and system design. You do not need to be an ML researcher, but you should be able to understand research outputs and make sound architectural decisions about how they are deployed and integrated into production systems.
Experience designing systems where reliability, explainability, observability and auditability are important engineering requirements, rather than afterthoughts.
A history of making high-impact technical decisions in environments where requirements are ambiguous, trade-offs are complex and the correct path is rarely obvious.

Staff AI Engineer

Key skills