Full-Stack AI Engineer — Cloud-Native & AI-Driven

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

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