Full Stack AI Software Engineer (Contract)

The engineer will work closely with Business Users, Product Owners, Solution Architects, Business Analysts, Quality Engineers, DevOps Engineers, Security Teams and Operations Teams throughout the software delivery lifecycle, from idea validation and prototyping through production adoption and ongoing improvement.

Software Development

• Design, develop, test, deploy and maintain enterprise-grade applications using Java with Quarkus and/or Spring Boot, Python, React, TypeScript and RESTful APIs.

• Develop scalable backend services, responsive frontend applications, reusable components, common libraries and well-defined APIs.

• Apply clean architecture, secure coding, design patterns and appropriate domain modelling to produce maintainable solutions.

• Design and implement cloud-native applications using containerised architectures and automated deployment practices.

Specification-Driven Development

• Work with users, Product Owners and Business Analysts to translate business needs into clear specifications, user stories, acceptance criteria, API contracts and technical designs.

• Apply Specification-Driven Development using structured requirements, OpenAPI specifications, Domain Driven Design (DDD), Behaviour-Driven Development (BDD) and Test-Driven Development (TDD).

• Maintain traceability from business requirements and specifications through implementation, automated testing, security validation and production release.

• Use specifications as living engineering assets that support code generation, testing, documentation and change impact assessment.

Proof of Concept and Proof of Value

• Partner with business users, Product Owners and application teams to identify opportunities for digital transformation, automation, AI adoption and platform modernisation.

• Design and implement Proof of Concept exercises to validate technical feasibility, integration patterns, security controls and architectural assumptions.

• Conduct Proof of Value exercises with stakeholders to demonstrate user outcomes, operational benefits, productivity improvement and delivery viability.

• Develop prototypes, reference implementations and reusable accelerators to shorten technology evaluation cycles.

• Document findings, constraints, architecture options, risks, recommendations and an adoption roadmap to support evidence-based decisions.

AI-Assisted Software Development

• Use approved AI-assisted development capabilities to improve engineering productivity, code quality and delivery consistency.

• Apply Large Language Models to support code generation, unit-test generation, documentation, code review, refactoring and requirements-to-code traceability.