AI System Design Specialist

About the role

The AI System Design Specialist is responsible for shaping the technical design and architecture of AI‑enabled solutions from the outset of delivery.
This role operates at the front of the delivery lifecycle, working closely with Product Experience Leads, Pod Leads, and Principal Engineers to ensure that every initiative is designed as an AI‑native, scalable, and maintainable system from day one.
Unlike traditional roles, this position combines deep system design expertise with hands‑on engineering capability, actively prototyping and validating ideas through code to ensure high‑quality, practical designs. Ensure that all solutions delivered within the AI organisation are correctly designed upfront, AI‑enabled by default, and technically sound—balancing flexibility, scalability, cost, and long‑term sustainability

What you’ll be doing

1) Early‑Stage System Design (Critical)
• Engage at the initiation stage of every project or initiative to define the overall system design.
• Translate business and product intent into coherent, end‑to‑end technical designs.
• Ensure solutions are designed as AI‑native systems, incorporating AI capabilities from the outset rather than as an afterthought.
2) AI‑Enabled Design Thinking
• Embed AI considerations into all designs, including: agent‑based workflows, model integration patterns,context and retrieval strategies and cost and performance trade‑offs
• Ensure solutions align with broader Context, Platform Mesh, and AI Economics capabilities.
3) Hands‑On Prototyping & Validation
• Develop proof‑of‑concepts (PoCs), prototypes, and experimental implementations to: validate design assumptions, test feasibility and performance, explore alternative technical approaches
• Actively write and review code to ensure designs are practical and implementable, not theoretical.
4) End‑to‑End System Integrity
• Define how different components (applications, agents, APIs, platforms) interact as a complete system.
• Ensure designs are: scalable, resilient, secure and maintainable over time
• Prevent fragmented or inconsistent technical solutions across pods.
5) Collaboration with Engineering Leadership
• Work closely with: Principal Engineers (technical depth and standards), Pod Lead Engineers (delivery feasibility), Platform Mesh and Context Engineers (platform alignment)
• Ensure alignment between design intent and delivery execution.
6) Standards & Best Practices
• Define and promote design patterns, reusable components, and engineering standards.
• Encourage reuse and modularity, avoiding duplication of capabilities across pods.
• Continuously refine design approaches based on learning and emerging practices.
7) Technical Risk & Decision Support
• Identify technical risks early and propose mitigation strategies.
• Guide decision‑making on: build vs reuse vs integrate, complexity vs speed trade‑offs, long‑term vs short‑term design decisions

Essential Skills / Experience

• Strong experience in designing complex, distributed software systems.
• Hands‑on software engineering expertise with the ability to prototype quickly.
• Deep understanding of modern architectures, APIs, and integration patterns.
• Experience working across multiple teams or products to drive design consistency.
• Strong problem‑solving and structured thinking abilities.

Desirable Skills / Experience

• Experience with AI / ML / LLM‑based systems, including practical implementation patterns.
• Familiarity with agent‑based architectures and AI orchestration frameworks.
• Exposure to cloud‑native architectures and scalable platforms.
• Experience working in Agile / product‑led environments.