Full Stack AI Software Engineer (Contract)

sggovterp; Singapore; On-site; SGD 90,000 - 140,000; Full time

Job summary

sggovterp is seeking a highly motivated Full Stack AI Software Engineer to design, develop, test, deploy and maintain secure, scalable cloud-native applications. You will work across the software delivery lifecycle with Business Users, Product Owners and DevOps teams, applying Specification-Driven Development, AI-assisted tooling and modern cloud practices to deliver value.

Responsibilities include building scalable backend in Java/Python, frontend in React/TypeScript, secure coding and

Responsibilities

• 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• 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 arch

Job description

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 arch