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.