Full Stack AI Software Engineer (Contract)

What the role is

We are seeking a highly motivated Full Stack AI Software Engineer to design, develop, test, deploy and maintain secure, scalable and cloud-native applications. The successful candidate will deliver business solutions using Specification-Driven Development (SDD), AI-assisted development, DevSecOps, Continuous Integration and Continuous Deployment (CI/CD), automated quality controls, and modern cloud engineering practices.

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

maintain enterprise-grade applications using Java with Quarkus and/or Spring Boot, Python, React, TypeScript and RESTful APIs. libraries and well-defined APIs. patterns and appropriate domain modelling to produce maintainable solutions. containerised architectures and automated deployment practices.  Owners and Business Analysts to translate business needs into clear specifications, user stories, acceptance criteria, API contracts and technical designs. OpenAPI specifications, Domain Driven Design (DDD), Behaviour-Driven Development (BDD) and Test-Driven Development (TDD). validation and production release. documentation and change impact assessment.  Owners and application teams to identify opportunities for digital transformation, automation, AI adoption and platform modernisation. validate technical feasibility, integration patterns, security controls and architectural assumptions. demonstrate user outcomes, operational benefits, productivity improvement and delivery viability. implementations and reusable accelerators to shorten technology evaluation cycles. recommendations and an adoption roadmap to support evidence-based decisions.  quality and delivery consistency. refactoring and requirements-to-code traceability.  responsible AI, information protection, security and human review requirements throughout AI-enabled software delivery.  and CI/CD release and deployment. detection and container scanning into delivery pipelines. checks and auditable evidence to prevent non-compliant artefacts from progressing through the delivery lifecycle. optimise pipelines, build processes, deployment automation and developer workflows.  and Engineering Enablement maintain reusable engineering templates, reference architectures, starter kits, common services and best-practice guides. applications and Infrastructure-as-Code. InnerSource practices that enable discoverability, contribution, code reuse, peer review, transparent ownership and cross-team collaboration. expectations and support processes for shared assets. implementation playbooks and developer productivity tools.  and Adoption Support deployment and operational support.  backup and disaster recovery requirements. appropriate engineering controls, documentation, automated tests and support arrangements. security and operations teams to remove adoption blockers and support successful production deployment.  contract and end-to-end automated tests. review and continuous refactoring practices. reviews and defect analysis, and implement sustainable corrective actions. risk and test effectiveness using objective engineering evidence.    secrets management, logging, metrics and distributed tracing services. operability and cost efficiency.

What we are looking for

Engineering or a related discipline.- 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/CD and 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 OpenAPI or Swagger, MCP, RAG architectures, LLM gateways, agentic workflows or related AI engineering patterns.
• Experience designing reusable common services, reference implementations, templates or developer platforms.
• Practical experience establishing or contributing to InnerSource programmes.
• Experience supporting the transition of prototypes or POCs into secure, supported production services.
• Experience in financial services, government or another regulated environment.
• Relevant cloud, software engineering, security or DevOps certifications.
• Strong problem-solving, systems thinking and analytical skills.
• Ability to translate user needs into testable specifications and practical engineering outcomes.
• Clear communication, facilitation and stakeholder-management skills.
• Software craftsmanship mindset with a strong focus on security, quality, reuse and automation.
• Curiosity, adaptability, ownership and commitment to continuous learning.