Full Stack AI Software Engineer (Contract)

[What the role is]

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.

[What you will be working on]

Software Development

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

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

• Apply clean architecture, secure coding, design patternsand appropriate domainmodellingto produce maintainable solutions.

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

Specification-Driven Development

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

• Apply Specification-Driven Development using structured requirements, OpenAPIspecifications, Domain Driven Design (DDD), Behaviour-Driven Development (BDD)andTest-Driven Development (TDD).

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

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

Proof of Concept and Proof of Value

• Partner with business users, Product Ownersand application teams to identifyopportunities for digital transformation, automation, AI adoptionand platform modernisation.

• Design and implement Proof of Concept exercises to validatetechnical feasibility, integration patterns, security controlsand architectural assumptions.

• Conduct Proof of Value exercises with stakeholders to demonstrateuser outcomes, operational benefits, productivity improvementand delivery viability.

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

• Document findings, constraints, architecture options, risks, recommendationsand an adoptionroadmap to support evidence-based decisions.

AI-Assisted Software Development

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

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

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

• Apply responsibleAI, information protection, securityand human review requirements throughout AI-enabled software delivery.

DevSecOpsand CI/CD

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

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

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

• Troubleshoot and optimisepipelines, build processes, deployment automationand developer workflows.

InnerSourceand Engineering Enablement

• Create and maintainreusable engineering templates, reference architectures, starter kits, common servicesand best-practiceguides.

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

• Promote InnerSourcepractices that enable discoverability, contribution, code reuse, peer review, transparent ownershipand cross-team collaboration.

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

• Author engineering standards, onboarding guides, implementationplaybooksand developer productivity tools.

Productionisationand Adoption Support

• Guide application teams through the lifecycle from idea, prototype and POC to MVP, production deploymentand 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, backupand disaster recovery requirements.

• Refactor successful prototypes into production-grade solutions with appropriate engineeringcontrols, documentation, automated testsand support arrangements.

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

Software Quality Engineering

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

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

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

• Monitor code quality, technical debt, dependency riskand 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, secretsmanagement, logging, metricsand distributed tracing services.

• Engineer for performance, availability, resiliency, security, operabilityand cost efficiency.

[What we are looking for]

• Degree or Diploma in Computer Science, Software Engineering, Information Technology, Computer Engineeringor a relateddiscipline.

• At least five years of hands-on software development experience, including delivery of production-grade enterprise applications.

• Demonstrated experience across backend, frontend, automated testing, CI/CDand production support.

• Experience working with multidisciplinary Agile teams and engaging both technical and business stakeholders.

• Experience building AI-powered applications and integrating approved enterprise AI services.

• Experience with OpenAPIor Swagger, MCP, RAG architectures, LLM gateways, agentic workflowsor related AI engineering patterns.

• Experience designing reusable common services, reference implementations, templatesor developer platforms.

• Practical experience establishingor contributing to InnerSourceprogrammes.

• Experience supporting the transition of prototypes or POCs into secure, supported production services.

• Experience in financial services, governmentor another regulated environment.

• Relevant cloud, software engineering, securityor DevOps certifications.

• Strong problem-solving, systems thinkingand analytical skills.

• Ability to translate user needs into testable specifications and practical engineering outcomes.

• Clear communication, facilitationand stakeholder-management skills.

• Software craftsmanship mindset with a strong focus on security, quality, reuseand automation.

• Curiosity, adaptability, ownershipand commitment to continuous learning.