AI-Native Solution Architect

Solution Ownership & Technical Governance

• • • Defining the overall technical strategy and architectural direction.
    • Defining engineering patterns and guardrails for AI-assisted development.
    • Ensuring AI-generated solutions meet production engineering standards.
    • Leading decisions around application architecture, APIs, integrations, databases, AWS infrastructure, authentication, and deployment.
    • Evaluating architectural trade-offs based on scalability, security, maintainability, performance, cost, and delivery speed.
    • Providing technical guidance to engineers and QA.
    • Leading architecture reviews and technical assessments.
    • Identifying technical risks and defining mitigation approaches.

The role is accountable for key architectural decisions, including:

• • • API and integration architecture.
    • Data storage and modelling.
    • Cloud and infrastructure design.
    • Application architecture and codebase organisation.
    • Framework and platform selection.
    • Authentication and access-control approaches.
    • Data migration and reconciliation.
    • AI-related architecture and engineering controls.

Engineering Excellence

The AI-Native Solution Architect helps the team transition from a traditional SDLC towards an AI-native Agentic Development Lifecycle.

Key focus areas include:

• • • AI-assisted engineering practices.
    • Architecture and code quality.
    • Secure development.
    • Automated and risk-based testing.
    • End-to-end and synthetic scenario testing.
    • CI/CD and release controls.
    • Monitoring and observability.
    • Technical documentation and specifications.

Collaboration & Delivery Support

Works closely with Project Management, Business Analysis, QA, engineering, and client stakeholders.

Responsibilities include:

• • • Balancing delivery speed, scope, budget, and technical complexity.
    • Translating business and clinical needs into technical solutions.
    • Establishing a repeatable process for turning requirements into specifications.
    • Keeping the team focused on the underlying problem, not recreating legacy functionality.
    • Challenging unnecessary complexity and historical requirements.
    • Providing technical leadership to the engineering team.
    • Communicating technical trade-offs, dependencies, and risks.

Pre-Sales & Solution Design (optional involvement)

• • • Lead architecture reviews, discovery sessions, and technical workshops.
    • Define solution architecture, assumptions, constraints, and non-functional requirements.
    • Review technical findings and validate priority and severity.
    • Evaluate alternative approaches, including benefits, risks, effort, and cost.
    • Support estimation and prioritisation of technical initiatives.
    • Present and defend architectural recommendations to the client.
    • Research relevant technologies and engineering practices and provide recommendations.

Technology Office Contribution

The AI-Native Solution Architect contributes to the evolution of AI-native engineering practices across the organisation.

Responsibilities include:

• • • Sharing lessons learned from implementing ADLC in production.
    • Defining reusable AI-native engineering practices and guardrails.
    • Evaluating AI tools and approaches for broader organisational use.
    • Supporting architecture discussions for strategic initiatives.
    • Sharing knowledge through mentoring, workshops, and internal discussions.