Full Stack Engineer - AI Applications - A26283
On-siteSingapore, Singapore
Job Summary
Refactor prototypes and vibe-coded applications into production-grade solutions with clean architecture, secure authentication, and automated deployment pipelines. Design, build, and harden AI-enabled applications including chatbots, RAG solutions, workflow assistants, and agents while integrating approved Azure AI services. Collaborate with stakeholders to clarify use-case outcomes and implement full-stack capabilities across frontend, backend, APIs, and data integration. Apply secure and responsible AI patterns, develop reusable implementation templates, and build automated test suites for functional and prompt evaluation. Support production-readiness assessments covering security, privacy, and observability, then document solution designs and operating procedures. This 12-month fixed-term role sits within JTC's AI Portfolio & Acceleration Squad to drive governed, scalable AI adoption across business users and platform engineers.
Required Qualifications
- 7+ years of hands-on software engineering experience
- experience building, deploying and supporting enterprise or cloud-native applications
- Strong full-stack engineering capability using modern frontend, backend and API development frameworks such as React, TypeScript, Node.js, Python, .NET, Java or equivalent technologies
- Hands-on experience designing and integrating REST APIs, backend services, databases, authentication mechanisms and enterprise application integrations
- Practical experience building AI-enabled applications using large language models, RAG patterns, prompt engineering, embeddings, vector search, agents, workflow automation or AI orchestration frameworks
- Working knowledge of Azure AI services such as Azure OpenAI, Azure AI Foundry, Azure AI Search, Azure Machine Learning, Azure App Service, Azure Container Apps, API Management, Key Vault, Azure Monitor and Log Analytics
- Experience applying secure software development practices, including input validation, secrets management, least-privilege access, dependency scanning, logging, error handling and secure configuration
- Experience with modern authentication and authorization standards, including OAuth 2.0, OpenID Connect, SAML, JWT, RBAC and enterprise identity integration using Microsoft Entra ID
- Hands-on experience with CI/CD pipelines using Azure DevOps, GitHub, GitHub Actions, ShipHATS or equivalent platforms
- Familiarity with containerisation, cloud deployment patterns, environment promotion, deployment rollback and production support practices
- Ability to assess prototype quality and determine what must be rebuilt, hardened, monitored or redesigned before production release
- Good understanding of AI risks, including hallucination, data leakage, prompt injection, unsafe tool use, policy bypass, privacy risks and poor explainability
- Strong documentation, communication and stakeholder management skills, with the ability to explain technical design choices and production trade-offs clearly
- Comfortable working in an agile, product-oriented environment where solutions are delivered iteratively and improved through user feedback, platform patterns and governance review
Desired Qualifications
- Experience building chatbots, knowledge assistants, workflow agents, document intelligence solutions, recommendation assistants or AI-enabled internal tools
- Experience working with AI application frameworks such as LangChain, Semantic Kernel, LlamaIndex, AutoGen, CrewAI or equivalent orchestration tools
- Experience with vector databases or search technologies such as Azure AI Search, PostgreSQL with pgvector, Cosmos DB, Pinecone or similar platforms
- Experience with AI evaluation, prompt testing, red teaming, safety evaluations, grounding quality checks or responsible AI controls
- Experience integrating with Microsoft 365, SharePoint, Teams, Microsoft Graph, Power Platform or enterprise workflow systems
- Familiarity with public sector cloud environments, government security requirements, data classification, privacy and compliance obligations
- Exposure to observability, SRE practices, incident response, service health dashboards and production support models
- Experience using AI-assisted development tools such as GitHub Copilot, Microsoft Copilot, Claude, ChatGPT Enterprise or equivalent tools to improve engineering productivity, testing and documentation
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