Solution Architect (Agentic Development Lifecycle)

Hi! We are seeking for a Technical Leader/ Solution Architect responsible for defining and guiding the architecture of a next-generation UK healthcare platform.

What is the role about?
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.

How many stages of the interview are there?
Interview with the Recruiter – up to 30 min.
Technical Interview – up to 1 hour;
Final interview – up to 1 hour;

Hard skills
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

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.

Responsibilities

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.

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.