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]

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.

[What we are looking for]

Degree or Diploma in Computer Science, Software Engineering, Information Technology, Computer

Engineering

or

a related

discipline.

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

D

emonstrated 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.