全职 AI Application Engineer 工作, 薪水, Tap Growth Ai 公司招聘中 - Ricebowl

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.

Ready to make an impact? Apply now and let's innovate together!