Senior architecture leadership for AI-native engineering organisations — without the full-time hire.

Who this is for

This role fits a specific moment in an organisation's evolution.

You're likely a fit if:

• Your engineering team has adopted AI-assisted development and velocity has jumped — but so has architectural inconsistency
• You're introducing AI agents, RAG systems, or LLM-backed workflows into production and the operational model is still being figured out as you go
• Your AWS footprint, security posture, or platform architecture is evolving faster than any single person can hold in their head
• Your senior engineers are making architecture decisions in isolation because no one has the cross-cutting view
• You need independent technical judgment on strategic decisions — without the politics of an internal hire
• You're between architecture leadership hires, or not yet at the scale where a full-time chief architect is justified

The common thread is complexity outpacing the architectural capacity to manage it — whether that's a 15-person startup shipping AI features into production or a 500-person engineering organisation modernising its platform.

The problem

AI changed the economics of writing code. It has not yet changed the economics of operating, securing, or evolving the systems that code produces.

The result is a widening gap between how fast teams can ship and how well the resulting systems hold together:

• AI-assisted development produces code faster than review processes can absorb
• AI agents and orchestration layers are introduced without clear integration boundaries or failure semantics
• AWS environments accumulate complexity without coherent governance
• Engineering teams adopt powerful tooling without the architectural discipline to make it sustainable
• Working demos hide operational complexity that surfaces months later in production
• Architectural ownership fragments across teams, and nobody has the complete view

At small scale, these are manageable. At growing scale, they compound into reliability problems, security exposure, hiring friction, and a platform that becomes harder to evolve over time.

The teams that get this right treat AI-native engineering as an architectural discipline — not just a productivity tool.

What you get

Four areas of ongoing involvement, applied to the decisions in front of you.

AI-native engineering

This is where most of the work happens.

AI-assisted development is the largest shift in how software gets built in the last twenty years. Most organisations are increasing development velocity faster than they are improving the architecture, review, testing, and operational practices that velocity depends on.

The work in this engagement is helping teams evolve toward AI-native engineering without losing architectural integrity:

• Establishing engineering guardrails around AI-assisted development that preserve velocity without sacrificing maintainability
• Designing AI agents and orchestration layers with clear boundaries, failure modes, and operational ownership
• Improving review, validation, and testing practices for AI-generated code
• Building the architectural patterns that let teams deploy LLM-backed features to production with confidence
• Aligning architecture, delivery, and operations around workflows where AI accelerates engineering and humans retain accountability

The goal is not slowing AI adoption. The goal is making it durable.

Engagement model

Retained advisory with ongoing involvement across architecture, engineering, and operational decision-making.

Engagements are selective and usually limited to a small number of organisations at a time. The work depends on enough context to make useful judgments, and that doesn't scale across many clients simultaneously.

What success looks like

After 90 days of engagement, organisations typically have:

• A clear target-state architecture for their AI systems and the operational model around them
• Engineering guardrails for AI-assisted development that the team actually uses
• Reduced architectural drift across teams and initiatives
• Stronger internal technical judgment — senior engineers making better decisions independently
• A defensible AWS and security posture aligned to how the platform is actually evolving
• Clearer ownership of operational risk across AI, infrastructure, and platform layers

The objective is not dependency on an external advisor. The objective is an organisation that operates with better architectural judgment than it did before — and continues to, after the engagement ends.