Full Stack AI Software Engineer (Contract)

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.

• Develop and integrate AI-enabled application features using enterprise APIs and approved AI platforms.

• Apply responsible AI, information protection, security and human review requirements throughout AI-enabled software delivery.

DevSecOps and CI/CD

• Build and maintain GitLab CI/CD pipelines for automated build, test, security scanning, packaging, release and deployment.

• Integrate SonarQube, Nexus IQ, SAST, dependency scanning, secret detection and container scanning into delivery pipelines.

• Apply quality gates, policy checks and auditable evidence to prevent non-compliant artefacts from progressing through the delivery lifecycle.

• Troubleshoot and optimise pipelines, build processes, deployment automation and developer workflows.

InnerSource and Engineering Enablement

• Create and maintain reusable engineering templates, reference architectures, starter kits, common services and best-practice guides.

• Establish common templates for APIs, microservices, frontend applications, GitLab CI/CD pipelines, security scanning, AI-enabled applications and Infrastructure-as-Code.

• Promote InnerSource practices that enable discoverability, contribution, code reuse, peer review, transparent ownership and cross-team collaboration.

• Define contribution guidelines, repository standards, ownership models, versioning practices, documentation expectations and support processes for shared assets.

• Author engineering standards, onboarding guides, implementation playbooks and developer productivity tools.

Productionisation and Adoption Support

• Guide application teams through the lifecycle from idea, prototype and POC to MVP, production deployment and operational support.

• Assess production readiness across architecture, security, data protection, scalability, resiliency, observability, supportability, compliance and cost.

• Help teams address architecture review, security review, CI/CD automation, testing, operational acceptance, monitoring, incident readiness, backup and disaster recovery requirements.

• Refactor successful prototypes into production-grade solutions with appropriate engineering controls, documentation, automated tests and support arrangements.

• Collaborate with platform, infrastructure, security and operations teams to remove adoption blockers and support successful production deployment.

Software Quality Engineering

• Develop unit, integration, API, contract and end-to-end automated tests.

• Apply TDD, BDD, clean code, secure coding, peer review and continuous refactoring practices.

• Participate in code reviews, security reviews and defect analysis, and implement sustainable corrective actions.

• Monitor code quality, technical debt, dependency risk and test effectiveness using objective engineering evidence.

Cloud and Platform Engineering

• Deploy and support applications on cloud platforms such as AWS using Docker and Kubernetes or OpenShift.

• Use Infrastructure-as-Code and automated configuration to provide consistent, repeatable environments.

• Work with API gateways, messaging, event-streaming, secrets management, logging, metrics and distributed tracing services.

• Engineer for performance, availability, resiliency, security, operability and cost efficiency.