AI Application Engineer

Tap Growth ai; Singapore; On-site; SGD 120,000 - 180,000; Full time

Job summary

Tap Growth ai in Singapore is seeking a seasoned AI Application Engineer to lead design and delivery of AI-enabled applications and scalable APIs. You will refactor prototypes into production-grade solutions with secure authentication, robust services and automated deployment pipelines.

As part of a dynamic team, you will design, build and harden AI-enabled apps using LLMs, RAG and workflow automation, leveraging Azure AI services and enterprise APIs.

Qualifications

• 7+ years of hands-on software engineering experience across enterprise or cloud-native apps.
• Strong full-stack capabilities with modern frontend, backend and API development.
• Hands-on experience designing and integrating REST APIs and enterprise integrations.
• Practical experience building AI-enabled apps using LLMs, RAG patterns, embeddings or orchestration frameworks.
• Knowledge of Azure AI services and cloud deployment patterns.
• Experience with secure software development practices and modern authentication standards.

Responsibilities

• Refactor prototypes into production-grade solutions with secure APIs and automated deployment.
• Design, build and harden AI-enabled applications including chatbots and workflow tools.
• Collaborate with product owners to clarify use-case outcomes and production-readiness requirements.
• Implement full-stack capabilities across frontend, backend, APIs, data integration and observability.
• Integrate with approved AI services via Azure and enterprise APIs.
• Apply responsible AI patterns including prompt management and content safety controls.
• Develop reusable patterns and engineering playbooks for AI delivery across use cases.
• Support production-readiness assessments covering security, privacy and governance.
• Build automated test suites and observability for AI apps.
• Collaborate with platform engineers to deploy AI apps using approved cloud patterns and CI/CD.
• Document solution designs and operation guides for maintainability post go-live.

Skills

Senior engineer

Full-stack

React

TypeScript

Node.js

Python

REST APIs

Azure AI

CI/CD

Secure SDLC

AI applications

Job description

We\'re Hiring: AI Application Engineer!

We are seeking a skilled and innovative AI Application Engineer to join our dynamic team in Singapore. The ideal candidate will possess extensive experience in developing and implementing AI applications, with a strong focus on optimizing performance and enhancing user experiences.

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.