Solutions Architect
Role Overview
The AI-Native Solution Architect is a senior technical leader responsible for defining and guiding the architecture of a next-generation UK healthcare platform.
The role combines solution architecture, technical leadership, and AI-native software delivery. You will help evolve an existing AI-driven implementation into a secure, scalable, maintainable, and production-ready platform while preserving the delivery speed enabled by modern agentic development practices.
A key part of the role is shaping the Agentic Development Lifecycle (ADLC): how requirements are captured, AI agents handle implementation, generated solutions are reviewed, testing is performed, and changes are released.
The role is also highly client-facing and requires the ability to clearly explain architectural decisions, trade-offs, risks, effort, and business impact.
Requirements
Experience
- 7+ years of software engineering experience, including technical leadership responsibilities.
- 3+ years in a Solution Architect, Software Architect, Principal Engineer, Staff Engineer, Tech Lead, or similar role.
- Proven experience designing and delivering complex production systems.
- Experience evolving prototypes or early-stage platforms into production-grade solutions.
- Experience working directly with clients and senior technical stakeholders.
Technical Expertise
- Project technical stack: AWS cloud infrastructure, Python/FastAPI APIs and integrations, relational databases and data modelling, data migration/ETL, AI-assisted and agentic development tools, CI/CD, monitoring and observability.
- Strong understanding of software architecture and distributed systems.
- Experience designing scalable, secure, cloud-native solutions.
- Strong AWS experience.
- Deep knowledge of APIs, integrations, and third-party systems.
- Strong understanding of relational databases, transactional systems, data modelling, and data integrity.
- Understanding of data migration, ETL, and reconciliation approaches.
- Strong knowledge of security, performance, scalability, reliability, and maintainability.
- Ability to evaluate technology choices and architectural trade-offs.
- Practical experience with modern AI-assisted software engineering tools and workflows.
- Ability to critically review AI-generated code and identify architectural, security, scalability, and maintainability risks.
- Experience with AI-native or agentic development approaches.
Leadership & Consulting
- Ability to define technical vision and architectural direction.
- Ability to translate architecture into practical guidance for engineering teams.
- Strong decision-making and problem-solving skills.
- Ability to operate effectively with ambiguity and incomplete requirements.
- Ability to challenge existing solutions constructively and support recommendations with evidence.
- Strong stakeholder management and client-facing communication skills.
Communication
- Upper-Intermediate or higher English proficiency.
- Strong presentation, facilitation, and documentation skills.
- Ability to explain complex technical decisions clearly to both technical and business stakeholders.
Nice to Have
- Experience in Healthcare, HealthTech, FinTech, or another regulated industry.
- Experience with UK healthcare or NHS-related systems.
- Experience with Python, FastAPI.
- Hands-on experience with AWS Bedrock.
- Experience with AI/agent frameworks such as LangGraph, vLLM, Ollama, or similar.
- Experience with large-scale data migration or legacy modernisation.
- Understanding of clinical safety or medical software regulation.
Core Responsibilities
Solution Ownership & Technical Governance
The AI-Native Solution Architect provides technical ownership of the new platform and ensures that architectural decisions support both immediate delivery goals and long-term product evolution.
Responsibilities include:
- 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.
The role defines how AI agents are used across requirements, implementation, testing, review, and delivery, and ensures AI creates measurable delivery improvements rather than being added on top of an unchanged SDLC.
Collaboration & Delivery Support
The AI-Native Solution Architect 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 appropriate technical solutions.
- Helping establish a repeatable process for turning requirements into development-ready specifications.
- Keeping the team focused on solving the underlying business and clinical problem rather than recreating legacy functionality.
- Challenging unnecessary complexity and historical requirements where appropriate.
- Providing technical leadership to the engineering team.
- Communicating technical trade-offs, dependencies, and risks.
Pre-Sales & Solution Design (optional involvement)
The AI-Native Solution Architect supports architectural proposals and future platform initiatives with the client.
Typical involvement includes:
- Leading architecture reviews, discovery sessions, and technical workshops.
- Defining solution architecture, assumptions, constraints, and non-functional requirements.
- Reviewing technical findings and validating their priority and severity.
- Evaluating alternative approaches and explaining their benefits, risks, effort, and cost.
- Supporting estimation and prioritisation of technical initiatives.
- Presenting and defending architectural recommendations to the client.
- Proactively researching relevant technologies and engineering practices and bringing recommendations to the project.
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 a production environment.
- Helping define reusable AI-native engineering practices and technical guardrails.
- Evaluating AI tools, frameworks, and approaches for broader organisational use.
- Supporting architecture discussions for strategic initiatives.
- Sharing knowledge through mentoring, workshops, and internal technical discussions.