AI Application Engineer

Rapsys Technologies Pte Ltd.; Singapore; On-site; SGD 180,000 - 210,000; Full time

Job summary

Rapsys Technologies Pte Ltd. is seeking an experienced AI Application Engineer to design, build and deploy AI-enabled applications from prototype to production. You will work across frontend, backend and data integrations, leveraging Azure AI services and enterprise APIs to deliver scalable, secure solutions.

The role emphasizes secure development practices, observability, and collaboration with product owners to ensure production readiness and measurable business impact.

Qualifications

• 7+ years of hands-on software engineering experience, including enterprise/cloud-native apps.
• Strong full-stack capability using React, TypeScript, Node.js, Python, .NET, Java or equivalent.
• Experience designing/integrating REST APIs, backend services, databases and auth.
• Practical AI-enabled app experience using LLMs, embeddings, RAG, agents, automation.
• Working knowledge of Azure AI services and enterprise cloud tools.
• Secure software practices: input validation, secrets, least privilege, logging.
• OAuth2/OpenID/SAML/JWT/RBAC with Microsoft Entra ID integration.
• CI/CD pipelines using Azure DevOps, GitHub/Actions or equivalent.
• Containerisation/cloud deployment patterns and production support.
• Assess prototype quality and decide what to rebuild for prod.
• Understanding of AI risks: hallucination, data leakage, prompt injection.
• Strong documentation/communication for stakeholders.
• Comfort in agile, product-focused delivery with iterative improvement.

Responsibilities

• Refactor prototypes into production-grade solutions with clean architecture, secure authentication, robust APIs and automated deployment pipelines.
• Design, build and harden AI-enabled applications, including chatbots, RAG solutions, workflow assistants, agents and automation tools.
• Work with product owners to clarify use-case outcomes, user journeys, adoption metrics and production-readiness requirements.
• Implement full-stack capabilities across frontend, backend, APIs, data integration, authentication, logging and monitoring.
• Integrate applications with Azure OpenAI, Azure AI Foundry, Azure AI Search and internal data sources.
• Apply secure AI patterns, including prompt management, retrieval grounding, input/output controls and human-in-the-loop design.
• Develop reusable implementation patterns, starter templates and engineering playbooks for AI delivery.
• Support production-readiness assessments covering security, privacy, data classification, observability and handover.
• Build automated test suites for AI apps, including functional tests, regression tests and guardrails.
• Implement observability for AI apps: logs, model usage, latency, errors, metrics and cost.
• Collaborate with platform engineers to deploy AI apps using CI/CD, containerisation and API management.
• Document solution designs, operating procedures, patterns and support guides for maintainability.

Skills

Full-stack engineering

React

TypeScript

Node.js

Python

.NET

Java

REST APIs

Azure AI services

CI/CD

Security best practices

Agile

Stakeholder management

Job description

We're Hiring: AI Application Engineer!

Location: Singapore, Singapore

Work Mode: Work from Office

Role: AI Application Engineer

Key Responsibilities

• 1. Refactor prototypes and vibe-coded applications into production-grade solutions with clean architecture, maintainable code, secure authentication, robust APIs and automated deployment pipelines.
• 2. Design, build and harden AI-enabled applications, including chatbots, RAG solutions, workflow assistants, agents, automation tools and AI-assisted business applications.
• 3. Work with product owners and business stakeholders to clarify use-case outcomes, user journeys, operational ownership, adoption metrics and production-readiness requirements.
• 4. Implement full-stack application capabilities across frontend, backend, APIs, data integration, authentication, authorization, logging and monitoring.
• 5. Integrate applications with approved AI services such as Azure OpenAI, Azure AI Foundry, Azure AI Search, Azure Machine Learning, Microsoft Graph, enterprise APIs and internal data sources.
• 6. Apply secure and responsible AI application patterns, including prompt management, retrieval grounding, input/output controls, human-in-the-loop design, auditability and content safety controls.
• 7. Develop reusable implementation patterns, starter templates and engineering playbooks for AI application delivery across common use cases.
• 8. Support production-readiness assessments covering security, privacy, data classification, model behaviour, observability, cost, support model and operational handover.
• 9. Build automated test suites for AI applications, including functional tests, regression tests, prompt evaluation, response quality checks and guardrail validation.
• 10. Implement observability for AI applications, including application logs, model usage, latency, token consumption, errors, user feedback, cost and key business metrics.
• 11. Collaborate with platform engineers to deploy AI applications using approved cloud patterns, CI/CD pipelines, containerisation, API management, secrets management and monitoring baselines.
• 12. Document solution designs, operating procedures, reusable patterns, known limitations and support guides to ensure applications are maintainable after go-live.

Required Skills and Experience

• 1. 7+ years of hands-on software engineering experience, including experience building, deploying and supporting enterprise or cloud-native applications.
• 2. 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.
• 3. Hands-on experience designing and integrating REST APIs, backend services, databases, authentication mechanisms and enterprise application integrations.
• 4. Practical experience building AI-enabled applications using large language models, RAG patterns, prompt engineering, embeddings, vector search, agents, workflow automation or AI orchestration frameworks.
• 5. 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.
• 6. Experience applying secure software development practices, including input validation, secrets management, least-privilege access, dependency scanning, logging, error handling and secure configuration.
• 7. Experience with modern authentication and authorization standards, including OAuth 2.0, OpenID Connect, SAML, JWT, RBAC and enterprise identity integration using Microsoft Entra ID.
• 8. Hands-on experience with CI/CD pipelines using Azure DevOps, GitHub, GitHub Actions, ShipHATS or equivalent platforms.
• 9. Familiarity with containerisation, cloud deployment patterns, environment promotion, deployment rollback and production support practices.
• 10. Ability to assess prototype quality and determine what must be rebuilt, hardened, monitored or redesigned before production release.
• 11. Good understanding of AI risks, including hallucination, data leakage, prompt injection, unsafe tool use, policy bypass, privacy risks and poor explainability.
• 12. Strong documentation, communication and stakeholder management skills, with the ability to explain technical design choices and production trade-offs clearly.
• 13. Comfortable working in an agile, product-oriented environment where solutions are delivered iteratively and improved through user feedback, platform patterns and governance review.